C
Generative AI Engineer
Centraprise
2 hours ago
Full-time
On-site
Charlotte, North Carolina, United States
Overview:
We are seeking a hands-on Lead Agentic AI & GenAI Engineer to architect and productionize secure, autonomous and human-supervised AI solutions. The role emphasizes agent design, LLM application engineering, AI-assisted software delivery, evaluation, safety, and production operations, with strong data integration skills to connect governed enterprise sources. Responsibilities
Architect single-agent and multi-agent solutions using Google ADK, LangGraph/LangChain, Microsoft Semantic Kernel, AutoGen, OpenAI Agents SDK. Design agent workflows using Model Context Protocol (MCP), function calling, structured outputs, reusable skills, planning, state, memory, A2A handoffs, and human approval gates. Use Claude Code, GitHub Copilot agent mode/coding agent, Devin, Cursor, OpenAI Codex for implementation, testing, refactoring, documentation, and pull request workflows. Build grounded enterprise AI experiences using advanced RAG, hybrid retrieval, reranking, knowledge graphs, citations, context engineering, and governed workflows. Engineer reliable Python and TypeScript agent services with asynchronous execution, retries, checkpointing, idempotency, rollback, escalation, and long-running workflow patterns. Implement agent evaluation and LLMOps using task-success benchmarks, ARIZE tracing, prompt/version management, latency and token-cost controls, and production SLOs. Implement enterprise AI security including prompt-injection defense, least-privilege tool access, sandboxing, PII protection, content safety, auditability, and red-team, and policy enforcement. Integrate agents with enterprise APIs, databases, events, documents, and Systems of Record, partnering with product, operations, risk, cyber, architecture, and platform teams.
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We are seeking a hands-on Lead Agentic AI & GenAI Engineer to architect and productionize secure, autonomous and human-supervised AI solutions. The role emphasizes agent design, LLM application engineering, AI-assisted software delivery, evaluation, safety, and production operations, with strong data integration skills to connect governed enterprise sources. Responsibilities
Architect single-agent and multi-agent solutions using Google ADK, LangGraph/LangChain, Microsoft Semantic Kernel, AutoGen, OpenAI Agents SDK. Design agent workflows using Model Context Protocol (MCP), function calling, structured outputs, reusable skills, planning, state, memory, A2A handoffs, and human approval gates. Use Claude Code, GitHub Copilot agent mode/coding agent, Devin, Cursor, OpenAI Codex for implementation, testing, refactoring, documentation, and pull request workflows. Build grounded enterprise AI experiences using advanced RAG, hybrid retrieval, reranking, knowledge graphs, citations, context engineering, and governed workflows. Engineer reliable Python and TypeScript agent services with asynchronous execution, retries, checkpointing, idempotency, rollback, escalation, and long-running workflow patterns. Implement agent evaluation and LLMOps using task-success benchmarks, ARIZE tracing, prompt/version management, latency and token-cost controls, and production SLOs. Implement enterprise AI security including prompt-injection defense, least-privilege tool access, sandboxing, PII protection, content safety, auditability, and red-team, and policy enforcement. Integrate agents with enterprise APIs, databases, events, documents, and Systems of Record, partnering with product, operations, risk, cyber, architecture, and platform teams.
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