Senior AI Platform Engineer​/Python Engineer​/Developer

Job Title :
Senior AI Platform Engineer / Python Engineer / Developer
Duration: 12 months+
Location:
St. Louis, MO
- Hybrid-3 days/week on site in St. Louis

Local or willing to relocate before starting (relo candidates will get second priority)
2 interviews. Second interview must be in person in St. Louis.

• Lead the design and development of an enterprise AI platform that enables agentic workflows across structured and unstructured data. This hands-on role combines software development, platform architecture, AI strategy, and technical leadership to build scalable AI solutions and guide key technology decisions.
• 3 must-have skills: experience w/ production agentic systems. implementing, monitoring, and evaluating those systems.
• Nice-to-have skills:
  If they have worked with Agent Bricks in any meaningful capacity. Working with Lang Graph. Self-motivated and willing to help other teammates. Not looking for someone who just wants to “shut up and code”

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.

• 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

• 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 Lang Graph 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.
• Understanding of agent architecture patterns, prompt engineering, and LLM evaluation techniques.
• Experience with AWS in production environments.
• Experience designing and consuming REST APIs.
• Experience with Git, CI/CD, and modern software engineering practices.
• Knowledge of secure coding principles and responsible AI practices.
• Experience collaborating within Agile software development teams

• Experience with Agent Bricks.
• Experience with Microsoft Copilot extensibility and agent development.
• Experience with Lang Chain and related frameworks.
• Experience with MCP integrations.
• Experience with vector databases and semantic search.
• Experience with AWS Bedrock.
• Experience with Open Telemetry, Lang Smith, Grafana, MLflow, Deep Eval, or similar tooling.
• Experience deploying multi-agent systems.
• Experience with Terraform.
• AWS, Databricks, Microsoft, or AI-related certifications.

• Delivers AI solutions that create measurable business value.
• Treats evaluation and observability as first-class engineering disciplines.
• Balances innovation with reliability, scalability, and governance.
• Takes ownership of production systems and outcomes.
• Thinks holistically about data, architecture, monitoring, and continuous improvement.
• Collaborates effectively across technical and business teams.
• Continuously learns and adapts to evolving AI technologies.