Lead - Artificial Intelligence Engineer

Lead AI Engineer (Python / LLM / Agentic AI)Job DescriptionWe 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 ResponsibilitiesDesign, develop, and maintain scalable backend services and AI-powered applications using PythonDesign and implement AI agents and agentic workflows, including tool calling, orchestration, multi-step reasoning, and multi-agent collaborationBuild LLM-powered solutions using RAG, embeddings, vector databases, and structured outputsIntegrate LLMs with internal systems, enterprise APIs, and external services using MCP and other integration approachesEvaluate and optimize AI solutions for accuracy, reliability, performance, latency, and costDesign scalable architectures using asynchronous processing, message queues, caching, and event-driven patternsImplement AI security controls, including prompt injection protection, data privacy, access control, and safe tool executionBuild and deploy production-ready services with proper testing, monitoring, logging, and error handlingCollaborate with product and engineering teams to translate business requirements into technical solutionsLead technical design discussions, review code, mentor engineers, and establish engineering best practicesRequirementsJob RequirementsRequired Qualifications7+ years of professional software engineering experience, including at least 2 years of backend development using PythonStrong experience with Python, FastAPI, asynchronous programming, and PydanticHands-on experience developing and deploying LLM-powered applications in productionPractical experience with AI agents, tool/function calling, and agent orchestration frameworks, such as LangGraph, LangChain, or equivalent solutionsStrong understanding of RAG architectures, embeddings, semantic search, and vector databasesExperience integrating LLMs through APIs, including structured outputs, prompt engineering, and handling model failuresExperience evaluating LLM applications, including response quality, hallucinations, reliability, and performanceSolid understanding of distributed systems, message queues, caching, retries, idempotency, and persistent state managementExperience with PostgreSQL, Redis, Docker, and REST APIsStrong knowledge of automated testing, clean architecture, design patterns, and code quality practicesExperience designing and delivering major features or products from concept to productionStrong analytical, troubleshooting, communication, and technical leadership skillsExperience with Model Context Protocol (MCP) and building MCP servers or clientsExperience with advanced agent orchestration, multi-agent architectures, and human-in-the-loop workflowsExperience with Qdrant, pgvector, Pinecone, or similar vector databasesExperience with NATS, Kafka, or other event-driven messaging technologies