Material Bank logo
Material Bank

Search and sample materials from hundreds of leading brands. Order by midnight, receive by 10:30am.

Applied AI Engineer

AI EngineerMachine Learning EngineerFull TimeRemoteLeadTeam 201-500H1B SponsorCompany SiteLinkedIn

Location

United States

Posted

26 days ago

Salary

0

Seniority

Lead

Bachelor Degree8 yrs expEnglishCloudJavaScriptPythonTypeScript

Job Description

Applied AI Engineer

Material Bank

• Design, build, and deploy end-to-end AI-powered product experiences from concept through production. • Architect and implement scalable AI systems leveraging LLMs, embeddings, multimodal models, retrieval systems, agent frameworks, and modern data infrastructure. • Build production-grade multi-agent workflows and orchestration systems using frameworks such as LangGraph, LangChain, Mastra, and custom tooling. • Develop and optimize Retrieval-Augmented Generation (RAG) systems, including embeddings, vector search, retrieval pipelines, chunking strategies, and relevance tuning. • Build multimodal AI workflows that analyze and reason over images, creative assets, and visual datasets using modern multimodal LLMs, embedding models, and specialized tooling such as SAM2/SAM3. • Create AI-assisted experiences for search, discovery, content generation, personalization, and creative workflows across Material Bank’s platform. • Evaluate, refine, and improve AI-generated outputs for quality, tone, accuracy, and creative alignment through testing, iteration, and human-in-the-loop evaluation strategies. • Partner closely with Product, Design, Engineering, Data, and Executive Leadership to identify high-impact opportunities and translate ambiguous ideas into production-ready AI capabilities. • Make architectural decisions that balance speed, scalability, latency, cost, accuracy, and long-term maintainability. • Continuously evaluate emerging AI technologies, models, frameworks, and workflows to identify opportunities that create meaningful business and user value.

Job Requirements

  • 8+ years of experience building and shipping production software, including significant full-stack engineering experience.
  • Demonstrated success designing and deploying production-grade AI/ML systems and AI-powered product experiences.
  • Deep hands-on experience with LLMs, embeddings, multimodal AI systems, RAG architectures, and multi-agent frameworks such as LangGraph, LangChain, Mastra, or equivalent custom tooling.
  • Strong engineering fundamentals across backend systems, APIs, data pipelines, cloud infrastructure, and modern JavaScript/TypeScript and Python ecosystems.
  • Experience working with multimodal models, visual analysis systems, and image-based AI workflows at scale, including familiarity with modern image-generation tooling and services.
  • Strong systems thinking with the ability to balance trade-offs across latency, cost, scalability, accuracy, reliability, and user experience.
  • Proven ability to independently take ambiguous problems from idea to shipped product with minimal oversight.
  • Strong product instincts, visual sensibility, and a high bar for quality, usability, and craftsmanship in AI-generated experiences.
  • Genuine interest in creative industries such as architecture, design, fashion, media, photography, or art, with an appreciation for aesthetics and taste.
  • Open-source contributions, side projects, or publicly demonstrable AI work that reflects curiosity, experimentation, and passion for applied AI are strongly preferred.
  • Strong communication and collaboration skills, with the ability to work effectively across both technical and non-technical teams.

Benefits

  • Flexible PTO, Sick Days, Paid National Holidays, and even more (ask us about this when we connect).
  • We contribute to your medical, dental, vision and short-term/long-term disability plans and have a strong employee assistance program.
  • 401(k) eligible after your first 90 day's employed!
  • We sponsor multiple events throughout the year to help out our communities.
  • We’ll help you take your career to the next level. We want you to be creative and take initiative which will allow you to grow and create within the company. Most importantly, be the best at what matters!
  • With business units and employees across the globe, Material Technologies has embraced a hybrid working model allowing department leaders to decide on the best approach for their respective teams, whether that be remote, in person, or a little of both.

Related Job Pages

More AI Engineer Jobs

Role Description We are seeking a skilled AI/MLOps Engineer to join the innovative team at 99x Brazil. In this role, you will be responsible for designing, deploying, and maintaining scalable machine learning infrastructure and pipelines that enable rapid development and reliable deployment of AI models. You will work closely with data scientists, engineers, and product managers to ensure seamless integration of AI capabilities into production systems. You will play a crucial part in automating ML workflows, monitoring model performance, and optimizing resource utilization in cloud environments. Join us to help drive the future of AI-powered solutions in a fast-paced, collaborative environment. Responsibilities - Design and maintain monitoring and observability solutions for AI applications and ML pipelines - Track logs, metrics, and traces using tools such as CloudWatch, Datadog, or similar platforms - Develop evaluation and testing frameworks for prompts, models, and AI workflows - Perform regression testing and quality validation for LLM-based systems - Manage prompt experimentation, versioning, and A/B testing processes - Debug AI workflows, including model outputs, orchestration pipelines, and infrastructure failures - Support deployment, scaling, and maintenance of AI/ML infrastructure in production environments - Collaborate with engineering and product teams to improve system reliability and performance - Analyze production data and user feedback to drive continuous improvement of AI systems - Contribute to operational best practices, documentation, and incident response processes Qualifications - Experience with DevOps, SRE, MLOps, or AI infrastructure engineering - Strong understanding of monitoring and observability concepts - Hands-on experience with tools such as Datadog, CloudWatch, Grafana, Prometheus, or similar - Experience supporting AI/ML or LLM-based applications in production - Familiarity with prompt engineering, model evaluation, and experimentation workflows - Knowledge of cloud platforms such as AWS, Azure, or Google Cloud - Experience troubleshooting distributed systems and production pipelines - Proficiency in Python, scripting, or automation tooling - Strong analytical and problem-solving skills - Excellent communication and collaboration abilities Nice to Have - Experience with LLM orchestration frameworks - Familiarity with vector databases and RAG architectures - Experience with CI/CD pipelines for ML systems - Knowledge of Kubernetes, Docker, and infrastructure-as-code tools - Experience with AI governance, security, or compliance practices Benefits - Your pick when it comes to employment models: CLT/PJ/Cooperativa - We provide resources for you to grow and learn on the job, including online courses, mentoring, and the latest-gen laptops - A fully remote work environment with flexible working hours - Bonus for any referrals that we end up hiring

Brazil
Job Closed
Gugu Robotics logo

Senior AI Engineer

Gugu Robotics

The Future is Now; Beyond Boundaries, Beyond Imagination

AI Engineer26 days ago
Full TimeRemoteTeam 51-200Since 2016H1B No Sponsor

• Design, implement, and deploy ML/AI models end-to-end • Maintain and evolve AI systems in production • Bring an AI-forward coding mindset • Partner closely with product, engineering, and data teams • Translate technical tradeoffs into terms non-specialists can act on • Participate actively in code reviews and design discussions • Contribute to AI architecture decisions • Take ownership of meaningful work end-to-end • Raise the bar on engineering practices by supporting junior engineers

Colombia
Accelerant logo

Forward Deployed AI Engineer

Accelerant

Where True Partnerships Exist

AI Engineer26 days ago
Full TimeRemoteTeam 201-500Since 2018H1B Sponsor

• Work directly with key stakeholders in a consultative manner to identify pain points and start building solutions to simplify manual and redundant workflows. • Write the production code — this isn't a strategy role. • Build agents to automate grind. • Partner with function heads to design, test, and deploy AI-driven agents and workflow automations. • Own evals, observability, guardrails, and the 'is this thing actually working' layer. • Use AI coding assistants (e.g. Claude, Replit, Vercel, etc.), low/no-code platforms, and workflow automation tools to rapidly build and ship MVPs. • Own cloud infrastructure, CI/CD, and operational reliability for what you ship. • Lead vendor evaluations, negotiate modular solutions, and ensure clean integration with our data and AI platforms.

United States
Job Closed
Zartis logo

AI Engineer

Zartis

A Software Services Company

AI Engineer26 days ago
Full TimeRemoteTeam 201-500H1B No Sponsor

• Build and extend multi-agent systems and agentic workflows using frameworks such as AWS Agent Core and AWS Bedrock Flows (or equivalent orchestration tools). • Develop and integrate Retrieval-Augmented Generation (RAG) pipelines for internal tools. • Implement LLM-powered chatbots, assistants, and autonomous agents tailored to specific business use cases. • Collaborate closely with the Team Lead to understand requirements and translate them into reliable, scalable implementations. • Take existing proof-of-concept or in-progress AI systems and harden them to production-grade standards. • Pipeline AI components together within the AWS and Databricks ecosystem, ensuring reliable end-to-end data and model workflows. • Apply best practices in observability, logging, and monitoring for deployed AI systems. • Contribute to CI/CD processes for model and prompt deployment where applicable. • Mentor and support other engineers within AI. • Communicate progress, blockers, and technical decisions clearly to both technical and non-technical stakeholders. • Participate in technical discussions and contribute to architectural decisions for AI systems.

Europe