Agentic AI Developer

Client: ClientCharlotte, NC
$86

About The Position

Implement agents and sub-agents (planner, executor, critic, router) using Claude Agent SDK / Lang Graph build 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.

Requirements

  • 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).
  • 3–8 years of experience in software development or data engineering
  • Hands-on experience in Generative AI or LLM-based applications
  • Experience building APIs, microservices, or distributed systems
  • Bachelor’s or Master’s degree in Computer Science, AI/ML, Data Science, or related field

Responsibilities

  • Implement single-agent and multi-agent systems using frameworks such as LangChain, Semantic Kernel, CrewAI, AutoGen, or similar
  • Build applications using LLMs (Azure OpenAI, OpenAI, Anthropic, etc.)
  • Implement Retrieval-Augmented Generation (RAG) pipelines
  • Enable agents to coordinate and collaborate in multi-agent ecosystems
  • Build secure, scalable APIs and microservices to support AI agents
  • Develop evaluation frameworks for agent performance (accuracy, hallucination detection, response quality)
  • Monitor system behavior and continuously improve reliability
  • Optimize performance for latency, cost, and scalability
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