Consulting Engineer, AI Developer Productivity

Lenovo is seeking a Principal Architect, AI Developer Productivity to fundamentally accelerate the velocity and output of LATC's AI engineering teams. This is a senior individual contributor role with organization-wide architectural authority and influence-designed for someone who has led large-scale developer productivity or engineering platform efforts and knows how to translate that experience into measurable gains for a team of AI researchers and engineers.

You will design and drive the automation of developer workflows from end to end-embedding AI tooling, agentic systems, and intelligent automation at every stage of how LATC's engineers plan, build, test, and ship. You will own the technical vision for how the team works, not just what it builds, and you will be accountable for demonstrable improvements in throughput, cycle time, and engineering quality.

This role sits at the intersection of applied AI, platform architecture, and engineering culture. It demands someone who is equally comfortable white-boarding an agentic workflow architecture with senior engineers, hands-on in the toolchain, and presenting a productivity roadmap to executive leadership. If you are driven by the idea of making an elite team of AI developers dramatically more effective-through systems, automation, and architecture rather than headcount-this role is for you.

Responsibilities:

Developer Workflow Automation & Agentic Architecture

• Workflow automation: Architect and implement end-to-end automation of developer workflows-from requirements and planning through coding, review, testing, and delivery-using AI tooling and agentic systems to eliminate manual toil and accelerate throughput.
• Agentic workflow design: Design and deploy multi-step agentic pipelines that augment developer capacity: autonomous code generation and review agents, intelligent test synthesis, automated documentation, and context-aware developer assistance integrated directly into the engineering environment.
• Toolchain architecture: Own the architectural decisions behind the developer toolchain-coding assistants, AI-augmented IDEs, autonomous agents, and supporting infrastructure-ensuring components are composable, maintainable, and purpose-built for AI development workloads.
• Platform integration: Integrate AI productivity tooling deeply into the existing engineering platform-version control, issue tracking, code review, and runtime observability-so automation is ambient rather than opt-in.

Velocity & Productivity Strategy

• Productivity measurement: Define and own the metrics framework for engineering velocity and productivity-establishing baselines, tracking improvement over time, and connecting automation investments to measurable outcomes (cycle time, defect rate, deployment frequency, developer satisfaction).
• Technical roadmap: Develop and maintain a multi-horizon productivity roadmap that anticipates the evolution of AI capabilities and continuously raises the ceiling on what the team can deliver.
• Prioritization: Identify the highest-leverage automation and tooling opportunities across the LATC engineering organization and sequence investments for maximum near-term and long-term impact.

Organizational Leadership & Influence

• Architecture authority: Set and govern standards for AI tooling and automation practices across the engineering organization; act as the final technical voice on developer productivity architecture decisions.
• Engineering culture: Champion an automation-first engineering culture, coaching senior engineers and tech leads on agentic workflows, prompt engineering for developer contexts, and AI-augmented development practices.
• Cross-functional alignment: Partner with engineering leadership, product, infrastructure, and security to ensure productivity investments are well-integrated, compliant, and aligned with broader organizational direction.
• Executive communication: Translate productivity strategy and outcomes into clear, business-grounded narratives for senior leadership; surface blockers and decisions that require executive attention.
• Ecosystem intelligence: Maintain a continuously updated view of the external AI developer tooling landscape-evaluating emerging tools, frameworks, and agentic architectures-and feed that intelligence into roadmap decisions.

Required Qualifications:

• Master's degree in Computer Science, Engineering, or a related technical field.
• 15+ years of experience in software engineering, platform engineering, or developer productivity roles, with at least 3 years at a principal, staff, or architect level.
• Demonstrated experience designing and delivering large-scale developer tooling, platform, or automation programs that produced measurable gains in engineering velocity.
• Deep hands-on expertise with AI coding assistants, agentic workflow frameworks, and the emerging landscape of autonomous development tools.
• Strong architectural background spanning developer toolchains, automation pipelines, and the infrastructure that underpins them-able to make and defend principled technology choices at scale.
• Proven ability to lead through influence in a large, matrixed organization-building alignment across engineering teams, platform owners, security, and senior leadership without direct authority.
• Hands-on programming experience in one or more modern languages (e.g., Python, Go, TypeScript); fluency in the tools and workflows of professional software development.
• Strong command of engineering productivity metrics and the ability to build measurement frameworks that hold up under scrutiny.
• Executive-level communication skills: able to synthesize complex technical strategy into clear, decision-ready narratives for VP- and C-level audiences.

Preferred Qualifications:

• Experience building or operating agentic AI systems in a production engineering context-including multi-agent orchestration, tool use, and retrieval-augmented workflows.
• Background in AI/ML infrastructure or MLOps, with an understanding of the unique workflow pressures faced by AI research and engineering teams.
• Prior experience in a platform engineering leadership role with broad authority over toolchain standards and developer experience.
• Experience with organizational change management and engineering culture transformation at scale.
• Familiarity with Lenovo's internal structure or similar large-scale, globally distributed technology organizations.