Ai/Llm Engineer — Python, Langchain, Rag, Aws Lambda (Ezeiza)

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 Why You'll Want This Role Postúlate en Kit Empleo: kitempleo.com.ar/empleo/q3n40