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• Design, build, and maintain production-grade AI systems and customer-facing AI features • Develop agentic workflows using LLMs, retrieval systems, tools, APIs, and backend services • Build backend services, orchestration systems, automation, and infrastructure supporting AI-powered workflows • Design and implement retrieval-augmented generation (RAG) systems, including ingestion pipelines, embeddings, semantic retrieval, and context assembly • Integrate foundation models through platforms such as Amazon Bedrock or Agent Core • Develop robust prompting strategies, structured outputs, guardrails, and workflow logic for production use cases • Implement evaluation systems for prompts, agents, and workflows, including regression testing, trace review, golden datasets, and human QA processes • Monitor and improve production AI systems for quality, reliability, latency, observability, and cost efficiency • Debug AI behavior through logs, traces, evaluations, user feedback, and production telemetry • Collaborate closely with engineering, product, operations, and customer-facing teams to turn ambiguous requirements into reliable systems • Help establish strong engineering standards around testing, deployment, CI/CD, version control workflows, code review, and operational reliability • Mentor and collaborate with engineers across both software and AI disciplines • Evaluate emerging AI technologies pragmatically based on business impact, maintainability, and operational reliability
We help companies develop the world's most productive and admired workforces.
• Help scale modular tools, intelligent workflows, and production-grade RAG systems • Mature existing AI proofs-of-concept into reliable production systems while establishing engineering rigor through TDD, CI/CD automation, and operational monitoring • Partner closely with AI Champions across departments to create reusable automation capabilities
Portless fulfills e-commerce orders directly from China, shaving months of time and saving you $$$ along the way.
Role Description At Portless, we specialize in global delivery solutions for SMBs and enterprise merchants, enabling businesses to ship direct-from-factory from manufacturing hubs like China to destinations worldwide. As an AI Engineer, you will own the design, development, and deployment of AI-powered systems that make our operations faster, our team smarter, and our merchants more successful — from intelligent automation and agentic workflows to LLM integrations embedded across our product and internal tooling. If you're passionate about building AI systems that create real business impact, thrive in fast-moving environments, and want to work at the intersection of logistics and cutting-edge AI, we'd love to meet you. - Design and build AI-powered features across our B2B portal, internal tooling, and merchant-facing products — including LLM integrations, AI agents, and intelligent automations - Translate ambiguous business problems into well-scoped AI solutions, from prompt engineering and RAG pipelines to full agentic workflows - Build, evaluate, and iterate on AI systems using a rigorous experiment-driven approach — tracking quality, latency, and cost tradeoffs - Collaborate closely with product, operations, and engineering teams to identify high-leverage AI opportunities and deliver them end-to-end - Develop internal AI tooling and skill frameworks that empower non-technical teams to leverage AI in their daily workflows - Integrate with third-party AI APIs (Anthropic, OpenAI, etc.) and MCP-based tooling while maintaining security and reliability standards - Maintain observability over deployed AI systems — monitoring for regressions, prompt drift, and model performance degradation - Work independently in a remote environment with a strong sense of ownership and ability to ship with minimal oversight Qualifications - 3+ years of software engineering experience, with at least 1–2 years focused on building production AI or ML systems - Hands-on experience with LLM APIs (Anthropic Claude, OpenAI GPT, etc.) and prompt engineering best practices - Strong programming skills in Python and/or TypeScript/JavaScript; comfortable building both backend services and lightweight frontend interfaces - Experience building RAG pipelines, embedding workflows, or agentic systems using frameworks like LangChain, LlamaIndex, or similar - Familiarity with vector databases (Pinecone, Weaviate, pgvector, etc.) and semantic search patterns - Experience working cross-functionally with non-technical stakeholders to scope and deliver AI projects - Proven ability to evaluate AI output quality and build evals/testing frameworks for LLM-based systems - Logistics, supply chain, or B2B SaaS experience is a strong plus - Experience with MCP (Model Context Protocol), AI agent orchestration, or multi-step tool-use workflows is a bonus
GitLab, founded in 2011 and based in San Francisco, California, maintains a distributed team of professionals that work remotely across multiple continents. GitLab advocates for pr
• Diagnose business problems before building solutions • Own AI initiatives end-to-end, from stakeholder discovery and technical design through implementation, deployment, and iteration • Design, develop, and ship AI-powered solutions quickly • Improve organizational flow by building solutions that reduce bottlenecks • Integrate AI capabilities into existing systems and workflows • Partner closely with stakeholders across functions • Define and track success through business metrics and feedback loops
QuinStreet offers a decentralized online marketplace that empowers consumers by matching them with brands that meet their needs. A leader among “research and compare” networks,
Role Description We are looking for a Senior AI Developer & Cloud Architect to design, build, and own the AI-powered compliance scraping engine and cloud infrastructure layer for an internal platform monitoring up to 70,000 credit card offer pages per month. This is a hands-on, sole-builder contractor role that sits at the intersection of cloud architecture, AI engineering, and large-scale web scraping, with a clear mandate: deliver a production-grade system that detects compliance violations across issuer offer pages with high accuracy and controlled token costs. You will do this by: - Architecting the AWS environment from the ground up. - Building a containerized worker fleet that integrates Playwright rendering with Claude-powered contextual analysis. - Defining clean API contracts with the internal team that owns the Laravel control panel. This is not a managed-PaaS or prototype role. You will be accountable for end-to-end delivery — architecture, build, documentation, and knowledge transfer — owning the full scraping and AI pipeline from URL intake through compliance findings, screenshot evidence, and results delivery back to the portal. Responsibilities - Design and configure the production AWS environment (ECS/Fargate, SQS, API Gateway, RDS PostgreSQL, S3, IAM, CloudWatch) using infrastructure as code (Terraform or CDK). - Build a stateless, containerized worker fleet that integrates Playwright-based page rendering, structured rule evaluation, and Claude API analysis. - Implement token optimization strategies across the LLM pipeline — prompt engineering, context pruning, caching, model selection, and batching — with measurable cost outcomes. - Define and document API contracts, job payload schemas, and database write patterns with the internal Laravel portal team to enable parallel development. - Build third-party API ingestion and field-level diff-detection logic that automatically adjusts monitoring rules when product data changes. - Handle modern web rendering challenges at scale: JavaScript-heavy SPAs, interstitials, cookie consent overlays, dynamic content, viewport switching, and full-page screenshot capture. - Evaluate when LLM analysis is the correct tool versus a classifier or rules-based approach, and design the two-stage rule-engine-plus-AI pipeline accordingly. - Build and maintain a unit test suite covering all modules and APIs to ensure uptime and proper functionality. - Document every architecture decision, configuration, API contract, and operational procedure continuously — not as a final-week deliverable. - Deliver a complete runbook and knowledge transfer to the internal team at engagement close. - Operate independently end-to-end while coordinating closely with the internal portal team and reporting directly to the Senior Director, surfacing risks and trade-offs early. Requirements - Production backend Python experience, including async patterns, type hints, packaging, and testing. - Direct production experience designing and configuring AWS ECS/Fargate, SQS, API Gateway, RDS (PostgreSQL), S3, IAM, and CloudWatch, with infrastructure as code (Terraform or CDK). - Real shipped systems calling the Anthropic Claude API in production, with demonstrated experience in prompt design, structured output, error handling, and cost trade-offs. - Demonstrated track record reducing token spend on production LLM workloads, with specific before/after results you can walk through. - Working knowledge of other LLM providers sufficient to recommend cheaper or better alternatives for specific tasks. - Production Playwright experience at scale, including headless Chromium failure modes, network idle detection, dynamic content handling, viewport switching, and screenshot strategy. Selenium or Puppeteer experience does not substitute. - Machine learning fundamentals sufficient to evaluate when LLM analysis is the right tool versus a classifier or rules-based approach, and to reason about evaluation and false-positive rates. - Docker and containerization experience, including image optimization, ECR, and stateless worker design. - Ability to operate fully independently — no engineering team underneath you — while documenting continuously and coordinating cleanly with an internal team. Nice to Have - Experience with API ingestion and field-level diff-detection systems. - Laravel or PHP familiarity, enough to coordinate cleanly on API contracts with the portal team. - SOC 2 Type II compliance experience. - Salesforce API integration experience. - Regulated-industry experience (financial services, healthcare, or insurance). Benefits - The expected hourly range for this position is $80/hr - 100/hr. This hourly range is an estimate, and the actual hourly rate may vary based on the Company’s compensation practices. - The hourly rate may be adjusted based on applicant's geographic location. - This position is eligible to participate in the Company’s standard employee benefits programs, which currently include health care benefits. Company Description QuinStreet is an equal opportunity employer. We do not discriminate on the basis of race, color, religion, national origin, pregnancy status, sex, age, marital status, disability, sexual orientation, gender identity or any other characteristics protected by law. Please see QuinStreet’s Employee Privacy Notice here.
• Design, build, and maintain AI agents, integrations, and automations that reduce manual work, eliminate bottlenecks, and improve productivity across every department at Chipply. • Inspect Chipply's internal SaaS platforms (HubSpot, Microsoft 365, Confluence, Slack, and others) to understand what APIs, webhooks, and MCP connectors they expose, and use those surfaces to build the integrations each team needs. • Partner with leadership and department heads to identify and prioritize high-impact opportunities by mapping existing workflows and pinpointing where engineering effort — AI-powered or deterministic — will deliver measurable improvement. • Write production code. Build multi-tool and multi-agent systems that connect Chipply's internal platforms into seamless end-to-end processes. Make sound judgment calls on when AI is the right tool and when a deterministic solution is a better fit. • Build evals and monitoring for AI systems you ship, so output quality is measurable rather than assumed. • Serve as Chipply's internal AI champion by leading education, training, and adoption efforts, and by acting as the go-to engineering resource for AI questions, best practices, and emerging use cases. • Establish and maintain responsible AI governance, including guidelines for data handling, privacy, model and tool selection, output quality monitoring, and ethical use. • Continuously evaluate emerging AI tools, agent frameworks, model providers, and orchestration libraries; pilot promising technologies and recommend adoption decisions based on impact, cost, and fit. • Measure and report on the ROI of the systems you ship, tracking time saved, error reduction, cost impact, and quality improvements. • Develop and maintain documentation for the systems, integrations, and AI workflows you build so institutional knowledge scales with the company. • Serve as a liaison across Engineering, Finance, Sales, Marketing, Customer Support, and Product to align on technology needs and ship workflow improvements end to end. • Monitor security and compliance across the integrations and AI systems you build, collaborating with engineering and leadership to ensure data protection best practices are followed. • Continuously redefine the scope, priorities, and deliverables of the role as Chipply's needs and the broader AI ecosystem evolve.
Five Below is a store with unlimited possibilities where tweens, teens and beyond are free to Let Go & Have Fun!
Role Description At Five Below our growth is a result of the people who embrace our purpose: We know life is way better when you are free to Let Go & Have Fun in an amazing experience, filled with unlimited possibilities, priced so low, you can always say yes to the newest, coolest stuff! Just ask any of our over 27,000 associates who work at Five Below and they’ll tell you there’s no other place like it. It all starts with our purpose and then, The Five Below Way, which is our values and behaviors that each and every associate believes in. It’s all about culture at Five Below, making this a place that can inspire you as much as you inspire us with big ideas, super energy, passion, and the ability to make the workplace a WOWplace! Key Responsibilities - AI Architecture & Strategy - Define and own the enterprise AI architecture for retail use cases, aligning with business priorities and technology strategy. - Develop reference architectures, patterns, and standards for AI/ML and Generative AI solutions, with an emphasis on open-source-first design principles. - Translate retail business problems — across merchandising, supply chain, stores, marketing, and e-commerce — into scalable AI solution blueprints. - Partner with business and product leaders to identify and prioritize high-impact AI opportunities. - Champion open-source AI frameworks and tooling (e.g., Hugging Face, LangChain, LlamaIndex, Ray, MLflow, Feast) as the default approach before evaluating commercial alternatives. - Open-Source AI Architecture - Lead the selection, evaluation, and integration of open-source AI and ML frameworks into Company’s enterprise architecture. - Design reusable patterns for open-source LLM deployment, fine-tuning, and serving (e.g., vLLM, Ollama, llama.cpp, OpenLLM). - Establish governance standards for open-source model usage, including licensing review, security scanning, and model provenance tracking. - Build internal capability around open-source foundations to reduce vendor lock-in and accelerate experimentation velocity. - Evaluate and adopt emerging open-source agentic frameworks (e.g., AutoGen, CrewAI, LangGraph) for retail automation use cases. - AI Solutioning & Design - Architect end-to-end AI solutions, including data ingestion, feature engineering, model training, inference, and system integration. - Design AI systems for core retail domains such as: - Search, recommendations, and personalization - Demand forecasting, inventory optimization, replenishment, and allocation - Pricing and markdown optimization - AI assistants and copilots for store, merchandising, and supply-chain teams - Define integration patterns between AI services and retail platforms (POS, OMS, WMS, CRM, e-commerce). - Lead architectural reviews, ensuring solutions meet performance, scalability, security, cost, and reliability requirements. - AI Observability - Define and implement an AI observability framework covering model performance monitoring, data drift detection, prediction quality tracking, and system health across all production AI systems. - Establish real-time and batch monitoring pipelines for model inference using tools such as Evidently AI, Arize, WhyLogs, Fiddler, or equivalent open-source platforms. - Design standardized dashboards and alerting for model degradation, data skew, latency SLO breaches, and feature store anomalies. - Build feedback loop infrastructure to capture ground-truth labels and enable continuous model evaluation in production. - Define observability standards for GenAI and LLM systems, including hallucination rate tracking, prompt/response logging, latency percentiles, and cost-per-query attribution. - Partner with MLOps and Platform Engineering to embed observability as a first-class requirement in every AI system from Day 1. - AI Security - Serve as the AI security authority for Company, owning the threat model for all AI and ML systems in production. - Define and enforce secure-by-design standards for model development, training data handling, inference APIs, and GenAI integrations. - Architect defenses against AI-specific attack vectors, including prompt injection, model inversion, adversarial inputs, data poisoning, and supply chain risks in open-source model adoption. - Establish data privacy controls for AI pipelines, ensuring compliance with applicable regulations (e.g., CCPA) and internal data governance policies. - Lead AI red-teaming and adversarial testing exercises to proactively identify and remediate security gaps before production deployment. - Partner with Information Security, Legal, and Enterprise Risk to maintain an AI risk register and align AI security posture with the organization’s broader cybersecurity framework. - Define guardrails, content filtering, and human-in-the-loop safeguards for all customer-facing and associate-facing GenAI applications. - MLOps, GenAI & Governance - Establish MLOps and AIOps practices, including CI/CD for models, automated retraining, monitoring, drift detection, and cost controls. - Define standards for Generative AI and LLM usage, including multi-RAG architectures, MCP, and vector search. - Define prompt orchestration, tool-calling, and agentic workflow patterns. - Ensure AI solutions comply with data privacy, security, and responsible AI principles. - Partner with Security, Legal, and Enterprise Architecture to align AI solutions with governance and risk standards. - AI Productivity Tooling Mandate - Personally mandate and model the daily use of AI-native productivity tools across all architecture and delivery work. - Evaluate, recommend, and govern the enterprise use of tools including: - Microsoft Copilot – for productivity, code assistance, and enterprise knowledge retrieval - Cursor – for AI-assisted development and code generation within engineering workflows - Glean – for enterprise search, institutional knowledge management, and AI-powered information retrieval - Claude (Anthropic) – for complex reasoning, document synthesis, and agentic task automation - Equivalent or emerging AI productivity platforms as the market evolves - Define standards and guardrails for enterprise AI tool adoption, including data classification policies governing what information may be shared with each platform. - Train and upskill engineering and cross-functional teams on effective use of AI productivity tooling to multiply output and reduce time-to-delivery. - Technology Evaluation, Implementation & Delivery - Work closely with AI Engineers, ML Engineers, Data Engineers, and platform teams to ensure architectures are production-ready and executable. - Provide hands-on guidance during implementation, including reference code, pipelines, schemas, and infrastructure patterns. - Evaluate and recommend AI SaaS solutions, cloud services, and frameworks (AWS, Azure, GCP, Databricks, Snowflake, etc.). - Lead build vs. buy vs. open-source decisions and support vendor selection for AI capabilities. Qualifications - 9+ years of experience in software, data, or AI engineering, with 5+ years in AI/ML architecture roles. - Proven experience designing and delivering production AI solutions specifically in retail, e-commerce, supply chain, or consumer-facing industries — this is a non-negotiable requirement. - Deep hands-on expertise with open-source AI/ML ecosystem: Hugging Face Transformers, LangChain, LlamaIndex, MLflow, Ray, Feast, Evidently, or equivalent. - Strong proficiency in Python and ML frameworks (PyTorch, TensorFlow, scikit-learn). - Experience with modern data architectures: lakehouse, streaming, batch pipelines; platforms such as Databricks and Snowflake. - Demonstrated experience designing AI observability systems — including model monitoring, drift detection, and production feedback loops. - Working knowledge of AI security threat models, including prompt injection, adversarial attacks, and secure LLM deployment practices. - Hands-on experience with cloud platforms and managed AI/ML services (AWS SageMaker, Azure ML, Vertex AI, or equivalent). - Established practice of using AI productivity tools (e.g., Copilot, Cursor, Claude, Glean, or similar) in daily engineering and architecture work. - Excellent communication skills with the ability to explain complex architectures to both technical and business stakeholders. Preferred Qualifications - Experience building or scaling enterprise AI platforms or AI Centers of Excellence. - Contributions to open-source AI projects or published architecture patterns. - Experience with AI red-teaming, adversarial testing, or formal AI risk assessment frameworks. - Familiarity with retail-specific platforms: Manhattan WMS, Blue Yonder, Aptos POS, Salesforce Commerce Cloud, or equivalent. - Cloud or AI certifications (AWS ML Specialty, Azure AI Engineer, GCP Professional ML Engineer). Benefits Explore our benefits site to discover all the perks and support we offer! From health coverage to financial and personal wellness, we've got you covered—check it out today! benefits.fivebelow.com/public/welcome Company Description Five Below is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, age, national origin, disability, protected veteran status, gender identity or any other factor protected by applicable federal, state, or local laws. Five Below is committed to working with and providing reasonable accommodations for individuals with disabilities. If you need a reasonable accommodation because of a disability for any part of the employment process, please submit a request and let us know the nature of your request and your contact information. crewservices.zendesk.com/hc/en-us/requests/new BE AWARE OF FRAUD! Please be aware of potentially fraudulent job postings or suspicious recruiter activity by persons that are posing as a Five Below recruiters. Please confirm that the person you are working with has an @fivebelow.com email address. Additionally, Five Below does NOT request financial information or payments from candidates at any point during the hiring process. If you suspect fraudulent activity, please visit Five Below's Career Site to verify the posting. fivebelow.com/info/careers
Bringing peace of mind through better health to our customers and communities
Role Description BlueCross BlueShield of Tennessee is seeking a Senior AI Engineer to lead the design, development, deployment, and support of advanced AI solutions across the healthcare payer ecosystem. As part of the MLOps team, this position operates at the intersection of AI engineering, MLOps, and applied business problem-solving. The Senior AI Engineer will help evaluate and adopt emerging AI platforms, shape how AI solutions are architected and delivered, and collaborate with cross-functional teams to bring production-ready solutions to life. This role also plays a key part in mentoring engineers and establishing best practices for modern AI development. Why This Role Is Exciting: - End-to-End Impact — Work across the full lifecycle, from use case discovery and prototyping to delivering enterprise-scale AI solutions - Hybrid AI Role — Engage across AI engineering, Generative AI, and MLOps in a single, high-impact position - Modern Technology Exposure — Work hands-on with LLMs, evaluation frameworks, observability tools, and cloud-native platforms - Cross-Functional Collaboration — Partner with business and technical stakeholders to solve real-world challenges - Influence & Ownership — Help define standards, delivery models, and enterprise AI architecture - Culture of Innovation — Thrive in an environment that encourages continuous learning, experimentation, and collaborative ideation Note: This is a fully remote role, but final interviews at our Chattanooga, TN headquarters will be required. Sponsorship is available for this role. Qualifications - Bachelor's degree in STEM (Science, Technology, Engineering or Math) or related field or equivalent work experience required. - Master’s or PhD degree in a relevant field (e.g., Computer Science, AI, Machine Learning) strongly preferred. - 5+ years of relevant work experience in analytics, technology, software engineering, or healthcare (academic experience included), or related equivalent experience. - 3+ years of hands-on experience specifically with machine learning, deep learning, and preferably AI models (e.g., LLMs, diffusion models) is required. - Proven experience handling large, complex datasets to build and optimize sophisticated data science pipelines. - Deep experience with Machine Learning, Deep Learning frameworks (e.g., Pytorch, TensorFlow), Natural Language Processing (NLP), and associated libraries (e.g., Hugging Face Transformers). - Experience deploying and managing ML models in cloud environments (GCP Vertex AI preferred, AWS SageMaker or Azure ML acceptable). Requirements - Expert proficiency in Python and relevant data science/ML libraries. - Proficient in Microsoft Office (Outlook, Word, Excel, and PowerPoint). - Proven ability to architect, design, and implement complex systems. Demonstrated success leading technically challenging projects from conception through to deployment. - Exceptional ability to interpret and translate complex technical concepts into information meaningful to project team members, business personnel, and leadership. - Strong technical leadership and mentorship capabilities. - Must be able to communicate effectively and influence both technical and non-technical co-workers and stakeholders. - Highly organized, reliable, capable of managing multiple complex tasks, demonstrating an exceptional work ethic and strategic thinking. Company Description
Role Description Reporting to the Director of AI Engineering, the Lead AI Engineer is responsible for building, managing, and scaling Gifthealth's AI engineering function. This position combines hands-on technical work with team leadership: architecting and implementing agentic AI systems while building, developing, and leading a team of AI Engineers. The Lead AI Engineer drives technical decisions across LLM orchestration, RAG pipelines, and production AI infrastructure, while establishing engineering practices, R&D functions, and growing team capabilities. We are seeking a Lead AI Engineer to partner with ML Engineers, Prompt Engineers, and Product teams, ensuring alignment with organizational goals, operational excellence, and compliance standards. Key Responsibilities - Designs and builds agentic AI systems including LLM orchestration, RAG pipelines, and multi-agent frameworks - Leads, mentors, and grows AI Engineers, conducting one-on-one meetings, performance reviews, and identifying career development opportunities - Works with the Talent Acquisition team to recruit and hire AI Engineers through defining role requirements, screening candidates, and leading technical interviews - Establishes engineering practices, coding standards, and technical processes for AI systems - Partners with ML Engineers, Prompt Engineers, and Product on cross-functional AI initiatives - Drives build/buy/integrate decisions; evaluates AI tooling and vendor solutions Qualifications - Bachelor’s degree in computer science, AI/ML, or a related field OR 2–4 years of equivalent practical experience (Required) - Master’s degree in computer science or AI/ML (Preferred) - 6+ years of software engineering experience with 3+ years in AI/ML systems; 2+ years of management or tech lead experience; history of building and shipping production AI systems (Required) - Experience in healthcare or regulated industries; history of building teams from scratch; FDA or clinical trial software experience (Preferred) - Knowledge of LLM architectures and orchestration patterns; RAG systems and vector databases; production AI/ML system design; software engineering best practices; and leadership and management principles (Required) - Knowledge of the healthcare domain and HIPAA compliance; FDA software validation; human-in-the-loop ML systems; graph databases and knowledge graphs (Preferred) - Python and AI/ML development skills (Required) - LLM API integration skills (Required) - System architecture and design skills (Required) - Technical hiring and interviewing skills (Required) - Team leadership and mentorship skills (Required) - GPU-accelerated processing skills (Preferred) - Distributed systems skills (Preferred) - Cloud platform (AWS, GCP) skills (Preferred) - ML Ops tooling skills (Preferred) - Ability to balance hands-on building with team leadership (Required) - Ability to hire and develop engineering talent (Required) - Ability to drive technical decisions across teams (Required) - Ability to communicate with technical and non-technical stakeholders (Required) - Ability to navigate ambiguity in early-stage product development (Preferred) - Ability to establish engineering culture and practices from scratch (Preferred) - Ability to influence without authority across functions (Preferred) Work Environment - Location: Remote - Schedule: 8:00 A.M. to 5:00 P.M. Monday through Friday with night and weekend hours on occasion as determined by the needs of the business. - Regular meetings with internal AI Engineering, Prompt Engineering, ML Engineering, Software Engineering, ML Ops Engineering, Data Science, HR, and product management teams. This role may also have meetings with AI/LLM, cloud, and ML Ops vendor and technology partner representatives. - Must be able to remain in a stationary position for extended periods while writing or reviewing documentation - Must be able to work on a computer for the entire shift - Must be able to attend virtual meetings with cross-functional teams Employment Classification - Status: Full-time - FLSA: Exempt Equal Employment Opportunity (EEO) Statement Gifthealth is an Equal Opportunity Employer and prohibits discrimination and harassment of any kind. All employment decisions are made without regard to race, color, religion, sex, sexual orientation, gender identity, transgender status, national origin, age, disability, veteran status, or any other legally protected status. We celebrate diversity and are committed to creating an inclusive environment for all employees. If you do not meet every requirement but still feel you would be a great fit for this role, we encourage you to apply! Disclaimer This job description is intended to describe the general nature and level of work being performed. It is not intended to be an exhaustive list of all responsibilities, duties, or skills required of personnel. Gifthealth reserves the right to modify job duties or descriptions at any time.
Louco is the game changer in live entertainment. The first platform that truly understands what users want and delivers hyper-personalized event experiences.
Role Description At Louco, we’re reinventing how people find and experience events. We’re looking for a Full Stack Engineer to join our small, fast-moving team and help build the future of event discovery. - Build and scale full stack systems powering Louco’s event discovery platform — from backend infrastructure and APIs to intuitive frontend user experiences. - Own end-to-end user flows across the platform, ensuring users can seamlessly discover, explore, and attend events. - Develop and improve systems for personalized feeds, recommendations, venue discovery, notifications, search, and ticket integrations across multiple providers. - Help integrate AI into the product experience to improve discovery, personalization, recommendations, and content understanding. - Build scalable ticketing and checkout-related systems connecting users with events in the fastest and simplest way possible. - Collaborate closely with engineers, product, and design to turn ideas into fast, intuitive, and scalable product experiences. - Take ownership of features from idea to production — including architecture, implementation, testing, deployment, and performance optimization. - Improve platform scalability, reliability, and app performance as Louco continues to grow. - Work in a fast-moving startup environment where priorities evolve quickly and new challenges constantly emerge. - Help shape the future of how people discover and experience real-world events. Qualifications - You’re a strong full stack engineer who enjoys building scalable product experiences across frontend and backend systems. - You’re highly proficient with technologies like PHP/Laravel, React, Node.js, and TypeScript. - You actively use AI development tools like ChatGPT, Claude, Cursor, Copilot, or similar tools to move faster and work smarter. - You enjoy building end-to-end user experiences and can move seamlessly across product, backend, infrastructure, and integrations. - You move fast, stay calm under pressure, and are able to make smart and pragmatic decisions in fast-changing environments. - You care about clean architecture, scalability, performance, and building products with real user impact. - You take ownership, think independently, and enjoy solving difficult problems with limited resources. - You’re excited about helping reinvent how people discover and experience real-world events. Benefits - Performance-based bonuses for outstanding contributions - High ownership and real product impact - Work directly with the founders - Build the future of event discovery - Flexible remote work - Opportunity to grow with the company long term - Work with modern AI tools and workflows Company Description Louco is the game changer in live entertainment. The first platform that truly understands what users want and delivers hyper-personalized event experiences.
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Python, Distributed Systems, TypeScript, Assembly, AWS, Cloud
