Agentic AI Engineer

We respectfully request that 3rd parties refrain from contacting us regarding this posting. Overview LABUR is partnering with a client to find a Senior Agentic AI Engineer who will design, build, and deploy production-grade AI agents end to end. This role sits at the intersection of applied AI, software engineering, and product thinking — requiring someone who can reason through every layer of an agentic system, from security and guardrails to user experience and model lifecycle management. The ideal person thrives in a collaborative environment and brings a passion for continuous learning in a rapidly evolving GenAI landscape. ResponsibilitiesDesign, develop, and deploy agentic AI solutions end to end, making deliberate decisions around architecture patterns, security, data masking, ranking strategies, and user experience Build and optimize RAG pipelines leveraging vector databases, prompt engineering, context management, and agent memory strategies to deliver grounded, accurate responses Architect and implement multi-agent systems with capabilities such as tool use, planning, guardrails, and observability Deploy and fine-tune AI solutions in production, iterating on performance and reliability while managing model versioning and rollback strategies Implement human-in-the-loop workflows, guardrails, and governance controls to ensure safe and trustworthy agent behavior Evaluate and monitor LLM performance through drift detection, hallucination assessment, and observability frameworks to maintain production quality Collaborate cross-functionally with engineering, product, and stakeholder teams to translate business requirements into scalable AI capabilities QualificationsProven experience building AI agents end to end and deploying GenAI solutions into production, with a track record of fine-tuning and iterating on live systems Deep understanding of agentic system design — including security, data masking, ranking, model lifecycle management, versioning, guardrails, and human-in-the-loop patterns Hands-on experience with RAG architectures, multi-agent orchestration, prompt engineering, and agent memory and state management Strong Python proficiency and experience with agent orchestration frameworks such as LangChain, LangGraph, or CrewAI Familiarity with vector databases such as FAISS, Pinecone, Chroma, or pgvector, and with LLM evaluation, observability, and monitoring practices Cloud platform experience required; GCP, ADK, and Vertex AI are a plus Financial services experience preferred; collaborative mindset with a passion for continuous learning Compensation $75-$80/hr - Dependent on fit and experience