Principal AI Platform Engineer

Lynx

50d•Remote

About The Position

This role is for a builder-architect who can integrate multiple, partially mature AI tools into a single, disciplined platform. The ideal candidate will be proficient in engineering architecture, backend integration, cloud infrastructure, LLM tooling, and production hardening. The mission is to own the AI platform as the engineering backbone for AI-assisted certification and engineering workflows, ensuring it is secure, stable, measurable, and extensible for confident development and operation of new AI tools.

Requirements

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
Ability 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

Nice To Haves

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

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 a standard and secure approach to access AI assisted capabilities across the organization for certification workflows.
Mentor and coach other senior/intermediate engineers on the team, provide technical guidance, and conduct architectural review for trade-offs.
Help define the technical trajectory of the platform and AI tools.