Technical lead building safe, evaluable LLM agents and AI infrastructure for OpenLoop’s telehealth platform. Setting architecture, observability, retrieval, and model operations direction.
About the role
- Senior AI Engineer at a specialized AI engineering partner, responsible for designing and implementing production-ready AI systems. Collaborate in a cross-functional team to shape AI solutions for clients.
Responsibilities
- Design, implement, and deploy AI/ML systems end-to-end, from prototypes to hardened production services
- Build and maintain data pipelines, retrieval layers, and training/inference workflows that support LLM and other model types
- Develop and evolve retrieval-augmented generation (RAG) setups, including chunking strategies, embedding selection, and vector search design
- Implement and tune agentic / multi-step workflows that orchestrate tools, APIs, and models to complete complex tasks
- Add observability around AI behavior: evaluations, logging, metrics, and guardrails to monitor quality, drift, and failures
- Integrate models with existing application backends and APIs, and design clean interfaces for internal and external consumers
- Optimize systems for performance and cost (token usage, caching strategies, routing between models, etc.)
- Contribute to architecture decisions, code reviews, and technical strategy within your team
Requirements
- Over 4 years of professional software engineering experience, with at least 2 years working on AI/ML features in production environments
- Strong programming skills in Python (or similar languages) and solid software engineering fundamentals
- Hands-on experience building with modern LLMs and generative AI tooling (prompting, fine-tuning, RAG, or agents)
- Experience with at least one major cloud platform; depth with AWS is a plus
- Familiarity with AI/LLM orchestration frameworks and evaluation tools
- Experience designing and integrating REST or gRPC APIs
- Working knowledge of containerization and orchestration (Docker, Kubernetes)
- Understanding of security and safety concerns around AI systems (prompt injection, PII handling, access control, etc.)
- Comfort working in ambiguous problem spaces and making pragmatic tradeoffs under constraints
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