AI Product Manager

Responsibilities

• Strategic Planning and Roadmap: Defining and communicating the AI product vision and strategy aligned with company objectives. Identifying market opportunities, prioritizing features, and driving AI applications to enhance business operations.

• Product Development and Execution: Developing AI products hands-on, including coding and prototyping, while collaborating closely with other teams (DevOps, SRE, Admins, and product teams) to ensure alignment with technical feasibility and business requirements. Actively leading the recruitment process for the growing AI team, including crafting and evaluating technical assessments, interviewing candidates, and onboarding new team members.

• Data-Driven Analysis and Continuous Improvement: Monitoring product performance, defining KPIs, and leveraging user analytics to evaluate effectiveness and drive continuous improvements.

• Stakeholder Collaboration and Go-to-Market: Serving as the primary liaison between technical teams and business stakeholders, coordinating go-to-market strategies, managing external partnerships, and ensuring effective product documentation and communication.

• Innovation and Future Development: Keeping abreast of emerging AI technologies, promoting innovation, and incorporating new methodologies to maintain competitive advantages and product differentiation.

Achievements

I operate this role with a deliberately platform-first posture: build reusable primitives — knowledge retrieval, agent tooling, subscription and access infrastructure — and let multiple products, teams, and workflows compound on top of them. Alongside the AI products delivered under this role (linked from the Related Projects panel), the following are cross-cutting outcomes that made those products land at company scale:

• Company-wide AI adoption for technical teams. Led the rollout of corporate coding assistants (Claude Code) across engineering, QA, DevOps, and analyst roles — including one-command onboarding, a centralized support and announcements channel, and migration off fragmented predecessor tooling. Reached steady-state adoption across dozens of active corporate users and standardized "AI in the SDLC" across the technical organization.

• Grounded AI for non-technical teams. Established corporate ChatGPT Business as a standard tool for daily work and extended it into a grounded assistant by connecting internal knowledge sources through the retrieval backbone and MCP servers I shipped under this role. Non-technical teams now get source-linked answers from internal documentation and the project system of record without leaving the assistant they already use.

• AI hiring pipeline and evaluation design. Screened and evaluated 100+ AI-engineering candidates end-to-end (CV → practical tasks → interviews). Built and iterated role-specific evaluation rubrics and practical tasks so filtering converges on real production-delivery ability, significantly improving pipeline quality and reducing wasted interview time for other stakeholders.

• Reusability, extensibility, and cost governance. Every initiative was shipped as a primitive that additional consumers can plug into without rebuilding — retrieval platform, MCP tooling, account strategy. That kept incremental AI-feature delivery fast while keeping operational cost and vendor risk under explicit control.

Expanded Role and Contributions

• Ongoing foundations for compounding gains. Designing and building a developer/QA agent system and a two-level corporate-memory architecture (short-term context memory + distilled long-term memory of approved solutions and patterns), so AI workflows stop repeating the same mistakes and capture organizational learning across team changes.

• Open-source leverage into production. Converted personal R&D into internal leverage: open-source tooling I author feeds directly into the internal agentic workflows, keeping the company aligned with the fast-evolving MCP / agent ecosystem without depending on any single vendor.

For the AI products delivered under this role — AILA (AI Localisation Assistant), AIR API (shared retrieval backbone), and the YouTrack MCP Server — see the Related Projects panel.

This tenure at Spotware Systems is marked by a platform-first posture: delivering AI products that unlock concrete business outcomes while simultaneously building the shared primitives, access model, and organizational practices that let every subsequent AI initiative arrive faster, cheaper, and better grounded.