Agentic AI Developer

Production Agentic AI Features DeveloperBuild and ship production agentic AI features — agents, tools, prompts, evals, and integrations — against an established reference architecture.Required Qualifications:3–8 years of experience in software development or data engineeringHands-on experience in Generative AI or LLM-based applicationsExperience building APIs, microservices, or distributed systemsBachelor's or Master's degree in Computer Science, AI/ML, Data Science, or related fieldKey roles:Implement agents and sub-agents (planner, executor, critic, router) using Claude Agent SDK / Lang GraphBuild tools and MCP integrations, design clean tool schemas, idempotent operations, and robust error handling.Implement RAG pipelines: ingestion, chunking, embedding (Bedrock Titan), hybrid retrieval, citation rendering.Develop Fast API/Python services exposing agent capabilities (sync + streaming); integrate with SQL (Postgres) and object stores (S3).Write evaluation harnesses (golden sets, regression suites, LLM-as-judge) and trace/observe agent runs.Implement guardrails: input/output validation, schema enforcement, rate limiting, prompt-injection defenses.Participate in code reviews, pairing, and architecture discussions; own quality of the code you ship.Strong Python (FastAPI, async, Pydantic) or Node/TypeScript equivalent.Hands-on with at least one agent framework (Claude Agent SDK / Lang Graph / AutoGen).Practical experience with LLM tool/function calling, structured outputs, streaming.RAG implementation experience (pgvector / FAISS / OpenSearch).Git, CI/CD, containerization (Docker), and cloud basics (AWS preferred).Roles/Responsibilities:Implement single-agent and multi-agent systems using frameworks such as LangChain, Semantic Kernel, CrewAI, AutoGen, or similarBuild applications using LLMs (Azure OpenAI, OpenAI, Anthropic, etc.)Implement Retrieval-Augmented Generation (RAG) pipelinesEnable agents to coordinate and collaborate in multi-agent ecosystemsBuild secure, scalable APIs and microservices to support AI agentsDevelop evaluation frameworks for agent performance (accuracy, hallucination detection, response quality)Monitor system behavior and continuously improve reliabilityOptimize performance for latency, cost, and scalability