We are looking for a Principal AI Engineering Lead to own and drive our AI engineering maturity journey across a 100-person R&D, QA, and DevOps organization.
This is a hands-on individual contributor role with outsized influence – you will be the internal expert, practitioner, and change agent who takes us from inconsistent AI tool adoption to a fully agentic, multi-phase autonomous engineering capability.
You will not be managing people. You will be changing how 100 engineers work.
This role is equal parts engineering, enablement, and architecture. You will write real code, build real agent workflows, and make the abstract concrete – turning a defined AI maturity framework into daily practice across Java/JVM, Python, C#/.NET, Oracle PL/SQL, and C++ codebases.
Scope of your position:
AI Adoption & Enablement
Audit current AI tool usage across R&D, QA, and DevOps – identify where adoption is genuine vs. nominal
Establish and maintain CLAUDE.md-equivalent constitution files: encoding team conventions, architectural standards, testing patterns, and security policies so AI tools produce consistent, codebase-aware output from day one
Drive daily active usage above 70% across the engineering org, measured by tool telemetry – not seat count
Design and deliver hands-on enablement: prompt engineering, output validation, effective task decomposition, and AI-assisted debugging across our primary stacks (Java/Spring, Python, C#/.NET, C++, Oracle PL/SQL)
Run monthly retrospectives to surface what context AI is still missing and close those gaps systematically
Agentic Infrastructure & Workflow Engineering
Architect and implement the infrastructure that makes autonomous agent execution safe: sandboxed execution environments, audit logging for all agent actions, and state checkpointing for mid-task recovery
Build and enforce the specification discipline: structured spec templates with machine-verifiable acceptance criteria, spec completeness gates before agent assignment, and a lightweight spec-driven development workflow appropriate for our codebases
Stand up self-verifying test loops – agents that write tests, implement, run CI, and iterate to green without human intervention – with coverage gates enforced in CI (80%+ on AI-generated code paths)
Shift CI/CD pipelines to increase build throughput: automated rollback, feature flags for deploy/release decoupling, and tiered review workflows (auto-merge → single reviewer → full review)
Deploy and tune Claude Code as the primary agentic coding platform, alongside evaluation and integration of other tools (GitHub Copilot, Cursor, or equivalent) where they complement the workflow
This is a hands-on individual contributor role with outsized influence – you will be the internal expert, practitioner, and change agent who takes us from inconsistent AI tool adoption to a fully agentic, multi-phase autonomous engineering capability.
You will not be managing people. You will be changing how 100 engineers work.
This role is equal parts engineering, enablement, and architecture. You will write real code, build real agent workflows, and make the abstract concrete – turning a defined AI maturity framework into daily practice across Java/JVM, Python, C#/.NET, Oracle PL/SQL, and C++ codebases.
Scope of your position:
AI Adoption & Enablement
Audit current AI tool usage across R&D, QA, and DevOps – identify where adoption is genuine vs. nominal
Establish and maintain CLAUDE.md-equivalent constitution files: encoding team conventions, architectural standards, testing patterns, and security policies so AI tools produce consistent, codebase-aware output from day one
Drive daily active usage above 70% across the engineering org, measured by tool telemetry – not seat count
Design and deliver hands-on enablement: prompt engineering, output validation, effective task decomposition, and AI-assisted debugging across our primary stacks (Java/Spring, Python, C#/.NET, C++, Oracle PL/SQL)
Run monthly retrospectives to surface what context AI is still missing and close those gaps systematically
Agentic Infrastructure & Workflow Engineering
Architect and implement the infrastructure that makes autonomous agent execution safe: sandboxed execution environments, audit logging for all agent actions, and state checkpointing for mid-task recovery
Build and enforce the specification discipline: structured spec templates with machine-verifiable acceptance criteria, spec completeness gates before agent assignment, and a lightweight spec-driven development workflow appropriate for our codebases
Stand up self-verifying test loops – agents that write tests, implement, run CI, and iterate to green without human intervention – with coverage gates enforced in CI (80%+ on AI-generated code paths)
Shift CI/CD pipelines to increase build throughput: automated rollback, feature flags for deploy/release decoupling, and tiered review workflows (auto-merge → single reviewer → full review)
Deploy and tune Claude Code as the primary agentic coding platform, alongside evaluation and integration of other tools (GitHub Copilot, Cursor, or equivalent) where they complement the workflow
Requirements:
7+ years of software engineering experience, with at least 2 years of hands-on work with AI-assisted or agentic coding workflows in production environments
Deep, practical experience with Claude Code (constitution files, skills, subagents, hooks, headless/agentic mode) and familiarity with the broader AI coding tool landscape
Fluency in two or more of our primary stacks: Java/Spring, Python, C#/.NET, C++, or Oracle PL/SQL – enough to earn the trust of engineers working in those languages and to diagnose where AI tooling struggles
Strong understanding of test infrastructure and CI/CD: coverage gates, automated rollback, pipeline design for high throughput, and what it takes to make a self-verifying agent loop reliable
Demonstrated ability to write and enforce machine-parseable specifications: structured acceptance criteria, EARS notation or equivalent, scope-bounded task definitions that agents can work from without re-prompting
Proven track record of cross-functional influence without authority – changing how a team works through demonstration, enablement, and trust, not mandate
Comfort operating in a large, heterogeneous codebase with varying levels of test coverage, documentation, and technical debt
7+ years of software engineering experience, with at least 2 years of hands-on work with AI-assisted or agentic coding workflows in production environments
Deep, practical experience with Claude Code (constitution files, skills, subagents, hooks, headless/agentic mode) and familiarity with the broader AI coding tool landscape
Fluency in two or more of our primary stacks: Java/Spring, Python, C#/.NET, C++, or Oracle PL/SQL – enough to earn the trust of engineers working in those languages and to diagnose where AI tooling struggles
Strong understanding of test infrastructure and CI/CD: coverage gates, automated rollback, pipeline design for high throughput, and what it takes to make a self-verifying agent loop reliable
Demonstrated ability to write and enforce machine-parseable specifications: structured acceptance criteria, EARS notation or equivalent, scope-bounded task definitions that agents can work from without re-prompting
Proven track record of cross-functional influence without authority – changing how a team works through demonstration, enablement, and trust, not mandate
Comfort operating in a large, heterogeneous codebase with varying levels of test coverage, documentation, and technical debt
This position is open to all candidates.
