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 — writing the production Python behind them rather than prototypes alone — while collaborating closely with other teams (DevOps, SRE, Admins, and product teams) to ensure alignment with technical feasibility and business requirements. Actively leading hiring for the growing AI team, including 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), what follows is how those products get built and the cross-cutting outcomes that made them land at company scale:

• Hands-on delivery of the production AI stack. The retrieval backbone and the agent-facing MCP tooling are not only products I scoped — I write the bulk of their Python myself: an async FastAPI service, LangGraph retrieval pipelines over a vector store with provider-fallback embeddings and cross-encoder reranking, FastMCP tool servers with per-request personal-access-token authentication, the pytest suites that cover them, the GitLab CI pipelines that run those tests before any tagged release deploys, and the Helm values that ship both services onto Kubernetes with horizontal pod autoscaling. LangSmith tracing is wired through the retrieval path and provisioned in the deployed environment's secrets, so request-level behavior is observable in production.

• 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.

• Hiring and technical evaluation for the AI team. Personally screened and interviewed 100+ candidates for AI-engineering roles (CV → practical tasks → interviews). Designed and iterated role-specific practical tasks and review criteria so my hiring decisions reflect real production-delivery ability, improving pipeline quality and cutting 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 the shared runtime our agents run on rather than one agent at a time: a self-hosted platform any team plugs its agents into, where a new assistant is a stored configuration — model, prompt, tools, subagents, per-tool approval gates — instead of a new codebase, and where scheduled and one-shot runs are isolated per user by construction. The developer and QA agents are its first workload, and its memory layer is 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.

• A governed front door for internal MCP servers. Building the corporate MCP gateway that puts every internal MCP endpoint behind one authenticated entrance instead of each server's own arrangements: corporate sign-in through a per-endpoint OAuth 2.1 authorization server, policy authorization evaluated on each individual tool call before it reaches the server behind it, per-endpoint tool filters, and audited invocations that carry the calling identity — the per-user, per-tool control a hosted assistant's own connector model does not offer. It is built on ToolHive as declarative Kubernetes custom resources under GitOps, and getting the MCP session and tool-filter behavior right meant reading the proxy's source and reporting two defects upstream.

• 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.