Principal AI Engineer - Autonomous Coding Agent & Developer Platform

Relocation:

• This position is open to candidates located anywhere in the United States who are willing to relocate to St. Louis.
• Relocation assistance is available for qualified candidates.

Location / Remote: Hybrid in St. Louis, MO; requires 3 days per week onsite after relocation

Employment Type: Permanent / Direct Hire / Full-time

Compensation: $180k base salary + 15% bonus (up to 30% based on performance)

Benefits:

• Comprehensive medical coverage with HSA/FSA options available for employees and family members

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• 401(k) with company match
• Paid time off (PTO)
• Access to employee wellness and benefits programs

As a Principal AI Engineer, you will lead the hands-on design and delivery of autonomous coding agent capabilities powered by GitHub Copilot to improve developer productivity and software quality. You’ll build the technical foundations for safe, scalable AI-assisted development workflows, from context and knowledge systems to tool and API integrations. Success in this role requires strong distributed systems engineering, cloud-native architecture, and practical experience building developer platforms that teams rely on in production.

Responsibilities:

• Architect and implement GitHub Copilot autonomous coding agent frameworks that support real engineering workflows end to end.
• Design and build guardrails, instruction hierarchies, and contextual knowledge systems to improve agent reliability, correctness, and maintainability.
• Develop integrations that enable AI agents to interact with repositories, developer tools, internal services, and APIs in controlled, auditable ways.
• Implement AI-assisted developer tooling and engineering productivity capabilities that measurably improve cycle time, quality, and developer experience.
• Design and deliver cloud-native, distributed services that support agent execution, orchestration, and context retrieval at scale.

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• Define microservices patterns, service contracts, and backend architectures that are secure, observable, and resilient.
• Establish CI/CD practices that support rapid, safe iteration for both application code and agent configurations (prompts, policies, and knowledge sources).
• Guide technical decisions across teams, balancing platform consistency with product delivery needs and operational constraints.
• Communicate architecture and tradeoffs clearly to stakeholders and provide practical technical direction to engineers building on the platform.

Required Skills:

• 7+ years of hands-on software engineering experience building and delivering production systems.
• Extensive experience designing GitHub Copilot autonomous coding agent frameworks, including guardrails, instruction sets, and contextual knowledge systems.
• Experience designing systems that enable AI agents to interact with development tools, APIs, and workflows.
• Experience implementing AI-assisted developer tooling or engineering productivity platforms.
• Experience designing and implementing cloud-native applications and distributed systems.
• Expertise in building microservices architectures and modern backend services.

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• Strong understanding of CI/CD pipelines and modern software development workflows.
• Proficiency with public cloud platforms such as Google Cloud Platform (GCP), AWS, or Azure.
• Ability to architect scalable systems and guide teams through complex technical decisions.
• Strong communication skills to explain technical concepts clearly.

Preferred Skills:

• Bachelor’s degree in Computer Science, Software Engineering, or equivalent practical experience.
• Deep expertise with the GitHub ecosystem, including GitHub Actions, GitHub Apps, webhooks, and API integrations.
• Experience building AI-powered developer tooling or productivity platforms.
• Knowledge of LLM-powered application design, including agent frameworks and context retrieval strategies.
• Background in distributed systems, event-driven architectures, and microservices platforms.
• Experience implementing observability, governance, and safety frameworks for AI-assisted systems.
• Proven ability to mentor engineers and influence architecture decisions across large organizations.