Principal AI Platform Engineer

Role Overview

This should be a builder-architect: someone who can take multiple partially mature AI tools and make them operate like one disciplined platform. The right person should be equally comfortable with engineering architecture, backend integration, cloud infrastructure, LLM tooling, and production hardening.

Key responsibilities

• Define and enforce the platform standard for how AI tools use orchestration frameworks, prompt assets, tracing, and metadata
• Bring existing advanced tools into alignment with shared platform conventions while preserving important agentic or workflow-specific behavior
• Build and maintain Azure-based production infrastructure, including networking, identity, secrets, storage, database, monitoring, and deployment patterns
• Implement infrastructure as code and CI/CD for sandbox-to-production promotion
• Deepen LLMOps capabilities, including prompt versioning, golden datasets, automated evaluations, cost tracking, feedback loops, regression detection, and release controls
• Own secure integrations with CodeBeamer, GitHub, and event-driven APIs or webhooks
• Establish operational discipline through logging, alerting, rollback, test coverage, runbooks, rate limiting, and supportability
• Partner with engineering, IT, security, and compliance stakeholders to support auditable AI-assisted workflows
• Own and evolve the Platform AI to provide standard and secure approach to access AI assisted capabilities across the organization for certification workflows
• Mentor and coach other senior/intermediate engineers on team, provide technical guidance, and conduct architectural review for tradeoffs
• Help define technical trajectory of the platform and AI tools

Qualifications

• 10+ years of relevant experience
• Bachelor’s Degree in engineering related discipline preferred
• Strong Python backend engineering and API integration experience
• Strong Azure platform experience, especially Container Apps, VNet/private endpoints, Entra ID, Managed Identity, Key Vault, PostgreSQL, ACR, and monitoring
• Hands-on experience with LLM application frameworks such as LangChain, LangGraph, or close equivalents
• Hands-on experience with LLM observability or evaluation tooling such as Langfuse or equivalent tracing and eval systems
• Experience building CI/CD and infrastructure as code with Terraform, Bicep, GitHub Actions, Azure DevOps, or comparable tools
• Experience securing internal platforms with RBAC, secrets management, service-to-service auth, webhook validation, rate limiting, and audit logging
• Able to design reliable multi-step or agentic workflows, including retries, state handling, guardrails, and output validation
• Strong operational judgment around testing, rollback, monitoring, alerting, documentation, and runbooks
• Must be a US Citizen

Strongly preferred

• Experience in regulated, safety-critical, aerospace, defense, medical, or similarly controlled environments
• Familiarity with DO-178C-style traceability, auditability, formal review workflows, or human-in-the-loop approval requirements
• Experience integrating with CodeBeamer, GitHub Enterprise, Jira, or similar enterprise engineering systems
• Familiarity with C/C++ code analysis or test-generation workflows
• Experience with prompt governance, change control, and evaluation datasets
• Some comfort with internal-tool UI work such as React, though this should remain secondary to platform, backend, and infrastructure strength