Local or willing to relocate before starting (relo candidates will get second priority)
2 interviews. Second interview must be in person in St. Louis.
Job Description:
About the Role
We are looking for a Lead AI Engineer to design, build, and operate enterprise AI solutions. You will help evolve our platform from Retrieval-Augmented Generation (RAG) applications to agentic AI systems that leverage reasoning, orchestration, tool use, and autonomous workflows.
You will work closely with platform engineers, architects, and business stakeholders to build scalable, reliable, and measurable AI solutions. A key part of the role is ensuring AI systems can be evaluated, monitored, and operated successfully in production.
What You Will Do
· Design, build, deploy, and support production AI solutions.
· Develop agentic workflows, tool integrations, and orchestration pipelines.
· Build and evolve RAG and agent-based architectures.
· Create evaluation frameworks to improve quality, groundedness, and reliability.
· Implement AI observability, tracing, and monitoring capabilities.
· Develop automated testing and regression validation processes.
· Integrate AI solutions with APIs, enterprise applications, and data sources. Design reusable AI patterns, frameworks, and components that increase platform scalability and team productivity.
· Establish engineering best practices and contribute to code reviews.
· Research and adopt emerging AI technologies where they provide business value.
· Partner with stakeholders to translate business problems into AI solutions
Required Skills
· 5+ years of software engineering experience.
· Proven track record of delivering enterprise-grade production software.
· Strong Python development skills.
· Experience building, deploying, and operating production AI systems.
· Experience designing and implementing RAG solutions.
· Experience with Databricks.
· Hands-on experience with LangGraph or similar agent orchestration frameworks.
· Experience building AI workflows that leverage tools, APIs, and external systems.
· Experience with AI evaluation frameworks and quality measurement.
· Experience implementing AI observability and tracing solutions.
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