Ai/Llm Engineer — Python, Langchain, Rag, Aws Lambda (Santa …, Santa Fe Capital

Project Brief
We're building AI-powered products, primarily leveraging off-the-shelf LLM APIs, while also partnering with our Applied Science teams to integrate custom models when needed.
You'll own end-to-end development of AI features — from prototyping to production — focused on reliability, accuracy, and scalable delivery.
Must-Haves
Strong Python engineering skills and experience delivering production systems
Hands‐on experience building with LLM APIs (OpenAI, Anthropic, etc.) and deploying AI features end‐to‐end
Experience with LangChain / LangGraph — including Prompt Engineering, Token Management, and Agent creation with tool‐based workflows
Experience building agentic workflows (tool use, routing, memory, safety/guardrails), ideally with MCP
Solid understanding of RAG concepts — retrieval, chunking, embeddings, re‐ranking, and evaluation
Practical AWS experience, especially AWS Lambda and serverless architecture patterns
Backend engineering experience: RESTful API design, webhook integration, JWT, pytest, and observability tools like Sentry
Experience doing Git repository analysis — understanding large codebases, debugging, refactors, and code health
Comfortable working in fast‐moving product environments with high ownership
Nice to Have
NextJS and TypeScript for frontend components and integration
AWS CDK for infrastructure‐as‐code and deployment
Broader AWS services knowledge: S3, CloudWatch, IAM, API Gateway
Experience with NoSQL databases
Experience integrating or serving custom models (inference, model endpoints, monitoring)
Familiarity with evaluation frameworks (offline tests, human feedback loops, regression suites)
Strong software engineering fundamentals: testing, CI/CD, observability, performance
What You Will Do
Plan and develop applications that include custom AI components — orchestration, evaluation, tooling, and integrations
Build intelligent agents using LLMs and the MCP framework to enable context‐aware automation and decision‐making
Automate internal workflows and operational processes using Python and AWS Lambda
Develop and improve RAG‐based chatbots to increase response accuracy, grounding, and consistency
Collaborate with Applied Science teams to productionize custom models and connect them with product systems
Design and maintain multi‐agent AI pipelines, including fallback and graceful degradation strategies for reliability
#J-*****-Ljbffr