About the role
We're looking for an experienced Agentic AI Engineer who has built and deployed production-grade AI applications using modern LLM frameworks. You'll design intelligent multi-agent systems, build scalable AI services, and work on cutting-edge Generative AI products.
Responsibilities:
- Design, develop, and deploy production-grade agentic AI applications.
- Build autonomous multi-agent workflows using LangGraph, LangChain, MCP, or similar frameworks.
- Develop RAG pipelines with vector databases for enterprise-scale AI applications.
- Design scalable Python/FastAPI backend services for AI platforms.
- Integrate OpenAI, Anthropic, Gemini, or open-source LLMs into production systems.
- Build tool-calling agents, orchestration pipelines, and structured AI workflows.
- Implement prompt engineering, evaluation frameworks, guardrails, and observability.
- Participate in architecture discussions, technical design reviews, and proof-of-concept initiatives.
- Mentor engineers, conduct code reviews, and drive engineering best practices.
- Collaborate with Product, Engineering, and Business teams to deliver AI solutions.
Requirements:
- Strong system design and architecture skills.
- Ability to independently own AI projects from design to production.
- Experience leading technical discussions and mentoring engineers.
- Excellent communication and stakeholder management skills.
- 7+ years of software development experience.
- Strong Python programming skills.
- Hands-on experience with LangChain and LangGraph.
- Experience building Agentic AI or Multi-Agent Systems.
- Strong understanding of RAG, embeddings, vector databases (Pinecone, Qdrant, ChromaDB, Weaviate, FAISS, etc. ).
- Experience with FastAPI, REST APIs, and microservices.
- Hands-on experience with OpenAI, Anthropic, Gemini, or similar LLMs.
- Experience with MCP (Model Context Protocol), tool calling, or AI orchestration frameworks.
- Good understanding of prompt engineering and LLM evaluation.
- Experience with cloud platforms (AWS/Azure/GCP) and Docker.
Good to Have:
- CrewAI, AutoGen, Google ADK, OpenAI Agents SDK, or similar frameworks.
- Kubernetes and CI/CD.
- LangSmith, LangFuse, Arize Phoenix, OpenInference, or AI observability tools.
- Experience with AI security, guardrails, and production deployment.