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Overview
LABUR is partnering with a client to find a Senior Agentic AI Engineer who will design, build, and productionize agentic AI systems that automate research, data collection, enrichment, validation, and decision-support workflows. This role centers on delivering reliable, scalable AI solutions using LLMs, retrieval systems, orchestration frameworks, and human-in-the-loop controls that drive measurable business outcomes. The ideal candidate combines strong engineering fundamentals with practical judgment around when agentic patterns are appropriate, with a focus on reliability, evaluation, traceability, and long-term maintainability. This role is fully remote.
Responsibilities
Design and develop agentic AI systems that automate research, enrichment, validation, and decision-support processes using LLMs, retrieval systems, structured data, external tools, and MCP services
Implement orchestration logic, routing, state management, retries, checkpointing, fallback strategies, and failure recovery mechanisms
Architect and maintain RAG, search, and data-ingestion pipelines across structured and unstructured data sources
Establish evaluation frameworks, testing strategies, observability, and quality controls for AI agents and workflows
Implement provenance, traceability, governance, and human-in-the-loop review processes where appropriate
Collaborate with product, data, platform, and engineering teams to identify high-value automation opportunities
Support production operations, incident response, root-cause analysis, and continuous improvement of deployed AI systems
Qualifications
Strong Python and production software engineering skills with hands-on experience building and operating production agentic AI systems
Experience with orchestration frameworks such as LangGraph, Airflow, AWS Step Functions, or Temporal, including tool use, function calling, MCP integration, and agent harness engineering
Proficiency designing state management, retries, checkpointing, routing, fallbacks, and failure recovery patterns
Deep understanding of retrieval, RAG, search, and data-ingestion pipelines across structured and unstructured data sources
Experience with AI evaluation, testing, observability, quality assurance, and human-in-the-loop workflows with provenance and data-quality controls