As a Product Director, you combine deep product leadership with strong hands‑on engineering and AI architecture credibility to guide teams as an internal product, engineering, and agility consultancy. You work side‑by‑side with engineers to design, build, and evolve high‑quality solutions—leveraging modern software engineering practices and applied AI (including Claude Code and internal AI platform) to improve how value is designed, built, and delivered.

You act as a coach and technical leader, capable of going deep into code, architecture, and AI‑assisted development workflows, while also aligning product, design, and delivery leaders on outcomes, trade‑offs, feasibility, and long‑term sustainability.

Job Responsibilities:

• Own and evolve the product and platform roadmap, vision, development, execution, risk management, and business growth targets, with a strong emphasis on engineering excellence and AI‑enabled delivery.
• Lead the full product life cycle—from discovery through delivery and continuous improvement—by adapting approaches, developing new capabilities, managing risk, and balancing cost, features, reusability, reliability, and scalability.
• Serve as a hands‑on technical and AI leader, working directly with engineers to:Architect solutions that effectively incorporate AI capabilities (e.g., Claude Code, internal LLMs).
• Establish best practices for AI‑assisted development, code quality, testing, security, and maintainability.
• Ensure AI is used pragmatically to improve developer productivity and system outcomes—not as experimentation in isolation.
• Act as a coach for cross‑functional teams, able to:Pair with engineers, review code, shape architectural decisions, and guide technical trade‑offs.
• Align product, design, and delivery leaders on scope, sequencing, feasibility, and value realization.
• Coach engineering teams on modern engineering and AI‑enabled practices, including:XP/TDD, automated testing, CI/CD, trunk‑based development.
• Pair/mob programming and AI‑assisted coding workflows.
• Reducing technical debt while clearly demonstrating business and operational impact.
• Drive adoption and evolution of operating models across the PDLC/SDLC, embedding AI‑enabled ways of working and enabling teams to continuously improve delivery, quality, and operational excellence through data‑driven inspection and adaptation.
• Coach and mentor product teams on solution generation, discovery techniques, experimentation, prototyping, and product adoption strategies—ensuring strong collaboration between product and engineering in an AI‑augmented environment.
• Own product and platform performance, accountable for investing in enhancements that achieve business, technical, and operational objectives.
• Monitor market and technology trends, including AI‑driven development and platform capabilities, to identify opportunities for differentiation and step‑change improvements in how products are built and delivered.

Required Qualifications, Capabilities, and Skills

• 10+ years of experience delivering software products, platforms, or technology solutions, with significant hands‑on engineering responsibility.
• Demonstrated ability to lead and coach engineers through direct technical engagement, combining:Deep product leadership
• Lean/Agile methodologies
• Modern engineering practices (XP, TDD, CI/CD, automation)
• Applied AI‑assisted development (e.g., Claude Code or similar tools)
• Strong hands‑on software engineering background, with the ability to:Read, write, and review production code.
• Shape system and solution architecture.
• Guide teams on pragmatic use of AI in real delivery contexts.
• Deep understanding of the product development life cycle, technical design, architecture, and data/telemetry, including how AI impacts these domains.
• Proven ability to influence and embed change across discovery, ideation, technical design, delivery, and value management in complex environments.
• Strong stakeholder management skills, able to operate effectively across engineering, product, design, risk, and leadership audiences.

Preferred Qualifications, Capabilities, and Skills

• Prior experience operating in a highly matrixed, complex enterprise environment.
• Full‑stack development experience, with exposure to modern cloud‑native architectures.
• Experience establishing or scaling AI‑enabled engineering platforms or internal developer tooling.
• Demonstrated success leading teams through architectural modernization or AI‑driven productivity transformations.