Lead - Artificial Intelligence Engineer

Lead AI Engineer (Python / LLM / Agentic AI) Job Description We are looking for an experienced Lead AI Engineer to design, develop, and deliver production-ready AI applications and backend services. The ideal candidate combines strong Python backend engineering skills with hands-on experience building LLM-powered applications, AI agents, and intelligent automation solutions. Key Responsibilities Design, develop, and maintain scalable backend services and AI-powered applications using Python Design and implement AI agents and agentic workflows, including tool calling, orchestration, multi-step reasoning, and multi-agent collaboration Build LLM-powered solutions using RAG, embeddings, vector databases, and structured outputs Integrate LLMs with internal systems, enterprise APIs, and external services using MCP and other integration approaches Evaluate and optimize AI solutions for accuracy, reliability, performance, latency, and cost Design scalable architectures using asynchronous processing, message queues, caching, and event-driven patterns Implement AI security controls, including prompt injection protection, data privacy, access control, and safe tool execution Build and deploy production-ready services with proper testing, monitoring, logging, and error handling Collaborate with product and engineering teams to translate business requirements into technical solutions Lead technical design discussions, review code, mentor engineers, and establish engineering best practices Requirements Job Requirements Required Qualifications 7+ years of professional software engineering experience, including at least 2 years of backend development using Python Strong experience with Python, FastAPI, asynchronous programming, and Pydantic Hands-on experience developing and deploying LLM-powered applications in production Practical experience with AI agents, tool/function calling, and agent orchestration frameworks, such as LangGraph, LangChain, or equivalent solutions Strong understanding of RAG architectures, embeddings, semantic search, and vector databases Experience integrating LLMs through APIs, including structured outputs, prompt engineering, and handling model failures Experience evaluating LLM applications, including response quality, hallucinations, reliability, and performance Solid understanding of distributed systems, message queues, caching, retries, idempotency, and persistent state management Experience with PostgreSQL, Redis, Docker, and REST APIs Strong knowledge of automated testing, clean architecture, design patterns, and code quality practices Experience designing and delivering major features or products from concept to production Strong analytical, troubleshooting, communication, and technical leadership skills Experience with Model Context Protocol (MCP) and building MCP servers or clients Experience with advanced agent orchestration, multi-agent architectures, and human-in-the-loop workflows Experience with Qdrant, pgvector, Pinecone, or similar vector databases Experience with NATS, Kafka, or other event-driven messaging technologies Show more Show less