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.
Responsibilities
- Design, 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
Qualifications
- Proven 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