Lead AI Platform Engineer

Codetech•Saint Louis, MO

About The Position

The Lead AI Platform Engineer designs, builds, deploys, and supports production AI solutions. The role develops agentic workflows, tool integrations, orchestration pipelines, and RAG- and agent-based architectures. It also creates evaluation frameworks to improve quality, groundedness, and reliability; implements AI observability, tracing, and monitoring; and develops automated testing and regression validation processes. The engineer integrates AI solutions with APIs, enterprise applications, and data sources, and designs reusable AI patterns, frameworks, and components to improve platform scalability and team productivity. The position establishes engineering best practices, contributes to code reviews, researches emerging AI technologies for business value, and partners with stakeholders to translate business problems into AI solutions. The role is suited to an engineer who delivers measurable business value, treats evaluation and observability as core engineering disciplines, balances innovation with reliability, scalability, and governance, and takes ownership of production systems and outcomes. The engineer thinks holistically about data, architecture, monitoring, and continuous improvement; collaborates across technical and business teams; and continuously adapts to evolving AI technologies.

Requirements

At least 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.
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.

Nice To Haves

Experience with AgentBricks.
Experience with Microsoft Copilot extensibility and agent development.
Experience with LangChain and related frameworks.
Experience with MCP integrations.
Experience with vector databases and semantic search.
Experience with AWS Bedrock.
Experience with OpenTelemetry, LangSmith, Grafana, MLflow, DeepEval, or similar tooling.
Experience deploying multi-agent systems.
Experience with Terraform.
AWS, Databricks, Microsoft, or AI-related certifications.

Responsibilities

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.
Take ownership of production systems and outcomes.
Collaborate across technical and business teams and continuously improve AI solutions.