Role Description PromptQL is hiring a Staff AI Software Engineer to help us build the technical backbone of our AI systems and product, PromptQL. This role exists because we are operating at the frontier of what's possible with conversation AI and we need someone who can think and build at that frontier with us. Our team has built impressive momentum with a small group of highly capable engineers and researchers. Now, we’re looking for someone who can bring deep technical leadership, broaden our capabilities, and help us scale our systems to provide accurate and reliable AI to Fortune1000 enterprises. This is not a narrow role. You’ll be: - Writing production code - Designing architecture - Optimizing performance - Shaping infrastructure - Contributing to safety mechanisms - Building our core stack - Rapidly designing and implementing features across the PromptQL AI assistant, the PromptQL data engine, and the PromptQL runtime infrastructure This is a rare opportunity to join a deeply ambitious and experienced team working on some of the most impactful technology of our time. If you're an A+ engineer who thrives in ambiguity, loves working across boundaries, and wants your work to directly shape how AI gets used in the real world—this role is for you. Qualifications - 6+ years of experience as a software engineer, with at least 1+ years building AI or ML-powered systems - Designed and shipped complex distributed systems at scale - Worked directly with large language models (LLMs) in production - Fluent in Python and/or a systems language - Comfortable leading technically: writing high-leverage design docs, setting engineering direction, and mentoring other senior engineers - Biased toward action and can operate with high autonomy - Care about quality, safety, and performance - Excited about our mission and energized by building foundational technology for the next generation of AI applications Requirements - You need a lot of structure, processes, or clearly defined roadmaps to succeed - You aren’t comfortable operating in ambiguity or changing priorities - You’ve never worked with AI/ML systems and don’t plan to upskill quickly - You aren’t confident writing production-grade Python or working in cloud-native environments - You prefer to work strictly within your lane and don't enjoy cross-functional collaboration - You aren’t excited about the pace and reality of building at an early-stage, high-velocity startup - You view engineering as separate from product and user experience - You’re not passionate about the transformative potential of AI or aren’t curious about its technical and societal implications Compensation - Salary $250,000 - $500,000 - Plus annual bonus - Large equity package Benefits - Medical, dental, and vision insurance to keep you healthy and thriving - Employee assistance programs for support when you need it - Retirement fund contribution matching to help you invest in your future - Donation matching to amplify your charitable impact - Paid time off to support your well-being - Monthly company-wide self-care day to recharge - Stock options so you can share in our success - Paid parental leave to support growing families - Commuter benefits to help you save on your journey to the office - Employee referral program to reward you for connecting us with great talent

Role Description We are seeking a versatile and highly skilled AI Engineer to join our fast-growing Full-Stack AI Solutions company. This role blends the expertise of building cutting-edge AI/ML models with the practical know-how of automation and agentic system integrations. You will design, develop, deploy, and optimize intelligent solutions that leverage the power of generative AI, automation platforms, and agent-based systems to enhance client operations, streamline workflows, and deliver measurable results. This is a unique opportunity to work across a diverse range of technologies—from fine-tuning transformer models to building real-world AI-powered automation stacks. If you’re passionate about pushing the boundaries of AI while creating real value through systems thinking and practical implementation, this role is for you. Key Responsibilities - AI/ML Development: - Design, build, and deploy machine learning and generative AI models for custom use cases. - Fine-tune and optimize large language models (e.g., GPT, BERT) using frameworks like Hugging Face Transformers. - Conduct ongoing research to stay ahead of advancements in AI/ML, including LLMs, generative AI, and transformer-based architectures. - Develop data pipelines for preprocessing, feature engineering, and model training. - Test, validate, and monitor model performance in real-world scenarios; iterate for reliability and accuracy. - Collaborate with cross-functional teams to embed AI into products, platforms, and services. - Mentor junior engineers and contribute to technical leadership. - Automation & Agentic Systems: - Design, implement, and maintain automation workflows using tools such as LangChain, LangGraph, LangSmith, LangFuse, n8n, ElevenLabs, and CMS integrations. - Engineer system-to-system integrations using APIs to enable intelligent process automation. - Apply prompt and context engineering techniques to enhance the performance of conversational AI tools. - Implement prompt management and QA processes for agents and assistants. - Explore and deploy Microsoft Copilot Studio, PowerApps, and PowerAutomate solutions (preferred but not required). - Continuously evaluate emerging AI and automation tools and assess their applicability in client use cases. - Collaborate with clients and internal teams to scope, deliver, and maintain agentic and automation solutions aligned with business goals. Qualifications - 2+ years of hands-on experience in AI/ML development, including training and deploying models in production. - Strong programming skills in Python, with proficiency in frameworks such as TensorFlow, PyTorch, Scikit-learn, and Hugging Face Transformers. - Experience working with AI tools like Langchain, OpenAI, or Stable Diffusion. - Practical experience with automation platforms and AI agent builders (as listed above). - Deep understanding of machine learning algorithms, neural networks, and NLP concepts. - Proficiency in using REST APIs for tool and platform integrations. - Familiarity with cloud-based AI/ML platforms. - Strong analytical skills and the ability to translate business problems into technical AI/automation solutions. Preferred - Experience working with Agentic AI frameworks, voice automation, and prompt tuning systems. - Exposure to MLOps best practices, including model versioning, CI/CD, and monitoring. - Knowledge of reinforcement learning, GANs, or edge AI deployments. - Familiarity with distributed computing frameworks such as Spark or Ray. - Background in AI agency workflows and client-facing solution delivery. Benefits - Competitive salary and performance-based incentives. - Flexible work environment: Remote-first. - Collaborative, innovation-driven culture with a commitment to personal and professional growth. - Opportunity to work at the forefront of AI innovation across industries and verticals.

• Take scoped use cases from the Director, AI Enablement and build working solutions: prompt layers, automation flows, lightweight agents, RAG implementations, and API integrations that connect AI tools to the systems business teams already use • Build working prototypes quickly - the goal is to show a team what is possible in days, not weeks, then refine based on what actually lands • Translate a documented workflow assessment into a technical design, select the right tools for the job, and own the build through to a deployable solution • Maintain solution documentation that is clear enough for handoff, iteration, and audit - not just for engineers, but for the business owner who will live with it • Spend time inside the functions you are building for - Finance, HR, Legal, Marketing Ops, Customer Success, and others - understanding how the work actually flows before you redesign it • Run working sessions with team members to map current-state workflows, surface friction points, and identify where AI creates real leverage versus where it creates noise • Build credibility with non-technical stakeholders by showing them working solutions, not decks - and by being honest about what will and will not work • Support user acceptance testing and early adoption, troubleshooting issues close to launch and iterating based on real usage • Build demos and proof-of-concepts that help the Director, AI Enablement make the case for a use case - or disqualify it early before resources are committed • Contribute to vendor and tool evaluations with a builder's perspective: not just whether a tool claims to do something, but whether it actually works in the context of a specific workflow • Feed learnings from the build process back into the program - what worked, what did not, what should be standardized across functions • Work on tactical quick wins and longer-horizon strategic builds at the same time, switching contexts across active use cases as priorities shift

Role Description We are looking for a highly skilled AI Systems Engineer who will be part of a collaborative and agile team that supports and builds modern, usable, and responsive applications for mission-critical U.S. federal government health IT solutions. The Senior AI Systems Engineer will operate in a fast-paced innovation environment, rapidly developing and validating proof of concepts for advanced AI and machine learning use cases. The role centers on: - Experimenting with cutting-edge analytics tools - Integrating them into a cloud-based analytics ecosystem - Helping move successful concepts toward operational deployment - Hands-on data exploration - Prototyping - Workflow automation - Contributing to secure, well-governed MLOps practices The Software Engineer will work closely with engineering and product teams to: - Evaluate emerging tools - Build technical demonstrations - Document integrations - Communicate platform updates in a highly collaborative setting Qualifications - 5+ years of relevant software / systems engineering experience - Software engineering background with deep experience building production-grade applications and services - Expertise developing agentic AI systems, including planning, tool use, multi-step reasoning, workflow execution, or autonomous decisioning logic - Hands-on experience designing and implementing MCP based integrations, tool interfaces, or model-driven service frameworks - Ability to translate ambiguous business or mission requirements into scalable AI-driven solutions, balancing technical feasibility with real-world impact - Proficiency with LLM development practices including fine-tuning, RAG integration, prompt engineering, and interaction models for agent workflows - Strong Python development skills and familiarity with distributed compute environments, APIs, microservices, and cloud-native architectures - Experience integrating agents or LLM driven components into cloud platforms (Azure, AWS, GCP) or large scale data ecosystems - Understanding of LLMOps/MLOps principles including versioning, testing, deployment automation, monitoring, and governance for agentic systems - Demonstrated ability to lead solution design, mentor developers, and communicate complex AI architectures to technical and non-technical stakeholders - Experience with version control and modern CI/CD practices (e.g., Git/GitHub), including automated testing, deployment pipelines, and release management for production systems - Ability to obtain/maintain a Public Trust clearance Requirements - Experience building or contributing to multi-agent systems or coordinated agent workflows - Familiarity with frameworks such as LangChain, LlamaIndex, Strands Agents, LangGraph, CrewAI - Knowledge of evaluating agent performance, implementing guardrails, or designing safe action execution patterns - Hands-on work with vector databases, embedding models, or advanced retrieval techniques - Experience integrating agentic components into large scale analytics or data platforms (e.g., Databricks, Snowflake) - Background in data exploration or data modeling to support agent-driven decision processes - Experience building AI focused prototypes or experimenting with emerging agentic patterns in rapid iteration environments - Familiarity with optimization techniques such as model routing, response validation layers, or inference acceleration - Familiarity with systems that handle sensitive data in accordance with HIPAA and federal security standards, including implementation of encryption, access controls, auditability, and governance for protected health information (PHI) within AI/LLM workflows Benefits - Top-tier benefits package to invest in your physical, mental, and financial health and wellness - Opportunities to learn new skills, seize new challenges, and advance your career - A culture of inclusion and opportunity for all