GeoSpatial Analyst MAP - GSANLT

Agentic AI Developer Location: Charlotte, NC (preferred for hybrid work; open to remote for strong candidates) Rate: $120.00 hourly 3 positions, do not submit duplicate candidates to Beeline 58088-1, 58089-1, 58090-1 Build 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 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 Key roles: • 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. • 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 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