Agentic AI Engineer

LABUR; Lone Tree (CO); On-site; USD 103,000 - 110,000; Full time

Job summary

LABUR is partnering with a client to hire a Senior Agentic AI Engineer in Lone Tree, CO, to design, build, and deploy production-grade AI agents end to end. The role blends applied AI, software engineering, and product thinking to create secure, user-friendly agentic systems.

The ideal candidate will implement guardrails, observability, and lifecycle management, with hands-on experience in RAG, memory strategies, and cross-functional collaboration to deliver scalable AI capabilities in

Qualifications

• Experience building AI agents end to end in production.
• Security, data masking, ranking, and model lifecycle management knowledge.
• Hands-on with RAG architectures, multi-agent orchestration, and agent memory/state management.
• Strong Python proficiency and experience with agent orchestration frameworks like LangChain, LangGraph, or CrewAI.

Responsibilities

• Design, develop, and deploy agentic AI solutions end to end, focusing on architecture, security, data masking, ranking, and UX.
• Build and optimize RAG pipelines leveraging vector databases, prompt engineering, context management, and agent memory strategies.
• Architect and implement multi-agent systems with tool use, planning, guardrails, and observability.
• Deploy and fine-tune AI solutions in production, managing model versioning and rollback strategies.
• Implement human-in-the-loop workflows, guardrails, and governance for safe agent behavior.
• Monitor LLM performance through drift detection, hallucination assessment, and observability frameworks.
• Collaborate cross-functionally to translate business requirements into scalable AI capabilities.

Skills

Python

AI agent experience

Agent orchestration

Tools

LangChain

LangGraph

CrewAI

FAISS

Pinecone

Chroma

pgvector

Job description

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