142. Senior AI Engineer – LLM and Agent Systems We’re looking for a Senior AI Engineer – LLM and Agent Systems to join Source Meridian About Source Meridian Source Meridian is a development software company that works to solve the industry’s most challenging problems in healthcare practices. We are laser focused on specific technologies in the healthcare and life science industries: Healthcare technology, artificial intelligence, and healthcare interoperability. About the Role AI Engineer with hands‑on experience in building production‑grade artificial intelligence systems using large language models (LLMs), agentic frameworks, and modern data infrastructure. The ideal candidate designs, develops, and deploys intelligent applications that leverage LLM orchestration, retrieval‑augmented generation (RAG), and memory‑managed architectures. What You’ll Do Design and implement agentic workflows using LangGraph and LangChain, including multi‑step reasoning, tool usage, and human‑in‑the‑loop patterns. Integrate LLMs (OpenAI, Anthropic, Google Vertex AI, open‑source models) via REST APIs and SDKs into scalable backend services. Build and maintain RESTful APIs (FastAPI) to serve AI‑powered functionality to frontends and external consumers. Architect and manage vector database solutions (Milvus) for semantic search and retrieval‑augmented generation (RAG). Design context engineering strategies, including prompt templates, dynamic context window management, token optimization, and context compression techniques. Implement short‑term memory (conversational buffers, sliding windows, summary memory) and long‑term memory (persistent vector stores, knowledge graphs, user profile stores) using MongoDB and vector databases. Store and manage structured and unstructured data in MongoDB, designing schemas that support conversation history, user state, and agent checkpoints. Evaluate and improve the quality of LLM responses through prompt engineering, few‑shot examples, guardrails, and automated evaluation pipelines. Collaborate with DevOps teams to containerize and deploy AI services using Docker, Kubernetes, and CI/CD pipelines on AWS or GCP. Required Qualifications Strong command of Python (3+ years) Demonstrable experience with LangChain and LangGraph (graph‑based agent orchestration, state management, conditional edges, parallel execution) Solid understanding of the fundamentals of LLMs: tokenization, embeddings, temperature/sampling, RAG Hands‑on experience with vector databases and embedding models for semantic search and retrieval pipelines Experience designing and consuming REST APIs; knowledge of authentication Proficiency in MongoDB (document modeling, aggregation pipelines, indexing strategies) Understanding memory architectures for conversational AI: summary memory, entity memory, and long‑term persistent stores Familiarity with context engineering: token budget management, hybrid search (sparse + dense) English level: B2 or higher Nice to Have Experience with assessment frameworks (RAGAS, Langfuse (LangSmith), customized assessments) Experience with streaming responses What We Offer Permanent contract Learning and continuous growth environment