Kyndryl
AIAgentDeveloper
“AI Agent Developer at Kyndryl. Skills: AI agents, large language model (LLM) applications, LangGraph, AutoGen, RAG pipelines, LLM integration, LLM evaluation, Docker, Kubernetes, Azure. Design and build production-ready AI agents with advanced planning, reasoning, and autonomous tool‑use capabilities. Architect stateful, multi-step agent workflows using modern orchestration frameworks such as LangGraph or AutoGen”
What You'll Achieve.
AI systems you build are trusted in production: observable, safe, cost‑aware, and resilient; Agent workflows are modular, explainable, and easy for teams to extend and maintain; RAG pipelines consistently deliver accurate, relevant, and timely results across diverse use cases; Engineering standards and patterns you establish accelerate delivery and reduce long‑term technical risk; Less experienced engineers grow through your guidance, code reviews, and technical leadership
Industry & Context.
solve real enterprise problems at scale; planning; reasoning; memory; tool‑use capabilities
What They're Looking For.
Must Have
7-10+ years in software engineering, Python expertise, Proven experience building agent-based or autonomous systems in production (not prototypes), experience with LLM APIs (OpenAI) including function calling and orchestration, Hands-on experience with frameworks such as LangChain, LangGraph, AutoGen, or CrewAI, Deep understanding of RAG architectures, vector databases (e. g. , Pinecone, Weaviate, Azure AI Search), and retrieval optimization, knowledge of LLM evaluation, prompt optimization, and safety techniques, Experience with containerization (Docker, Kubernetes), Experience with cloud platforms (Azure preferred), Familiarity with backend frameworks (e. g. , FastAPI), Familiarity with microservices architecture
Nice to Have
certify in all four major platforms
What You'll Do.
Design and build production-ready AI agents with advanced planning
and autonomous tool‑use capabilities
multi-step agent workflows using modern orchestration frameworks such as LangGraph or AutoGen
Build and productionize RAG pipelines
Integrate LLMs with enterprise systems by leveraging APIs
and external data sources
Continuously optimize RAG and agent performance
and guardrail frameworks for LLM systems
and scale AI applications using Docker
and Azure cloud services
Drive technical design decisions
Define best practices for agentic AI development
How You'll Work.
Team & Collaboration
mentoring engineers; code reviews; technical leadership
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