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AI Projects

AI Projects

AI Projects and AI Team Leadership

I build AI-based solutions and system integrations, and my goal today is to work as a project lead / team captain in this field. I treat AI not as a showpiece but as a tool that delivers measurable value inside business processes.

Who this is for

This is for leadership teams who want to bring AI into the company but need clarity on where to start, whom to hire and how to manage the risks — and for organizations looking for a project lead or team captain for an AI program already underway. The goal is not to say "we have AI" but to improve the work in a measurable way.

What I do

  • Designing and setting up AI agent processes
  • LLM RAG: knowledge-based applications that answer from company documents
  • LLM fine-tuning work
  • Integrating AI into existing web, mobile and backend systems
  • AI governance: risk, accountability and data controls, informed by my ISO/IEC 42001:2023 lead auditor training

Skill areas I work in: AI Agent Process, LLM RAG Process, LLM Fine Tuning; on the infrastructure side Node.js, MongoDB, Redis and Firebase.

How I manage work in this area

What AI projects lose most often is a clear objective: the model works but does not answer a business question. So I first write down which decision or process should improve, and then move forward with a small pilot.

Steps

  1. Goal and use case: which business question, for whom, with which data — and what is explicitly out of scope.
  2. Data and access review: the documents to be used, personal-data risk, authorization and retention rules are settled in the first weeks.
  3. Pilot: a small-scope AI agent or RAG flow is built and judged against real examples.
  4. Measure and decide: the team and decision makers review whether the pilot works; the decision to expand, stop or change direction is recorded in writing.
  5. Rollout and handover: delivered with integration into existing systems, usage guidance and a maintenance plan.

Deliverables

  • Scope and acceptance-criteria document
  • Data, access and risk register
  • Pilot results summary and expansion proposal
  • Working integration, technical documentation and a handover session

Methods and tools

I split the work into short sprints and show a working result at the end of each. Risks are recorded with an owner and a mitigation. Borrowing the framing of ISO/IEC 42001, the purpose, owners, data source and monitoring approach of an AI system are defined up front. This framework is my working method, not a company certification.

Common risks and how I handle them

  • Vague objective: Success criteria and out-of-scope items are written before the pilot starts.
  • Data privacy and authorization: Which data reaches the model and who can access what is settled in the first phase.
  • Scope creep: Every new request goes for approval with its effect on schedule and budget.
  • Going to real users too early: The pilot runs with a limited user group and results are reviewed before expanding.

Acceptance criteria

  • The pilot's success criterion is written as a business-relevant indicator, not "it looks good".
  • Source documents behind the model's answers are traceable; the basis of an answer can be shown.
  • Data access and personal-data rules are documented.
  • The team has the knowledge needed to operate and update the system without me.

What I will ask you before we start

  • Which business question or process do you expect to improve?
  • Where does the data live and who can access it?
  • Who is on the current team; which roles do we need to set up?
  • How will we measure success, and whose decision does it depend on?

Communication rhythm

I use a short weekly status note, a demo at the end of each sprint and written records for critical decisions. With teams across time zones I reduce meetings and lean on written, recorded communication.

Why it matters for leading AI teams

A common challenge at the head of an AI team is reconciling the pace of research with the pace of product delivery. My engineering background helps me question the team's estimates; my management view helps me tie those estimates to schedule, budget and risk. That is why remote roles at Head of AI, AI Program Director or CTO level are the positions I am looking for.

Frequently asked

How do you decide between prompting, RAG and fine-tuning?

I first check whether the goal can be met with RAG or prompt design; fine-tuning is proposed only when that falls short, and with a data and cost rationale.

Is this page a claim of AI certification?

No. The ISO/IEC 42001:2023 lead auditor course is personal training; this page describes how I work with that perspective.

To get started: describe your goal in two or three sentences via Contact or on WhatsApp (+90 531 679 10 14). For my overall method see Project Management, for my working rhythm How I Work, and for other areas Services. I work remotely, including across time zones — see the FAQ.