Two services, one point of contact

A fractional CTO when you need to decide and structure, an ML & AI engineer when you need to build. The two often go together.

Service 1

Fractional CTO

A senior technical lead, a few days a month, who makes technical decisions with you and sees them through.

Strategy and architecture

Stack choices, architecture, build or buy, a technical roadmap aligned with your goals.

Team and hiring

Role definitions, technical interviews, onboarding, leading developers and contractors.

Delivery practices

Code review, tests, CI/CD, development, staging and production environments.

Cloud, security and costs

AWS, Google Cloud or Azure, infrastructure as code, access and secrets management, backups, a hard look at the cloud bill.

AI and data strategy

Spot the use cases that pay off, get the data ready, decide whether to build, buy or wait.

Audit and due diligence

A technical assessment before a fundraise, an acquisition or a rewrite: risks, debt and priorities, stated plainly.

Format: a few days a month, over several months. A short audit is often enough to set the priorities and get started.

Service 2

Freelance ML & AI engineer

A senior engineer embedded in your team, or working independently on a defined deliverable, from prototype to production.

LLM agents and applications

Agents wired into your tools and APIs, structured outputs, evaluation, guardrails and human review. Built with PydanticAI or Amazon Bedrock AgentCore.

Your documents, searchable

Search, question answering and information extraction across your documents, contracts or knowledge base (RAG).

Machine learning

Scoring, forecasting, recommendation, anomaly detection. Models evaluated on your data, not on a benchmark.

Data engineering

ELT pipelines, dbt transformations, data warehouses such as Microsoft Fabric or PostgreSQL, Spark for large volumes.

Production and MLOps

FastAPI services, containers, CI/CD, model and cost monitoring. A model is only worth something once it’s used.

Python backend

Python APIs and services in a hexagonal architecture: tested, typed, easy to hand over.

Format: daily rate, or a fixed price for a defined deliverable. Remote, or on site in Montpellier and the south of France.

And often, both

Many engagements start on the CTO side: an assessment, priorities, a roadmap. Then the first AI or data component gets built with your team, by the same person who recommended it.

The upside: nothing gets lost between the person who advises and the person who delivers, and recommendations account for what implementation really costs.

How it starts

No big project in the dark. Scope fast, deliver early, and you stay in control at every step.

Book a call
  • 1

    A first call (30 min)

    Your context, your team, what’s blocking. Free, no commitment, and if Datooine isn’t the right fit you’ll know by the end of the call.

  • 2

    A written proposal

    Scope, format (fractional, daily rate or fixed scope), timeline and price. Clear and jargon-free.

  • 3

    The engagement

    Regular check-ins, code and documentation in your repositories, decisions explained.

  • 4

    The handover

    The goal is for your team to carry on without Datooine. Planning for that starts on day one.

Technologies

Examples, not a fixed list. Chosen when they fit, never on principle.

Python TypeScript FastAPI PydanticAI Amazon Bedrock AgentCore OpenAI TensorFlow Apache Spark dbt Microsoft Fabric PostgreSQL MongoDB Docker Kubernetes Terraform GitHub Actions AWS Google Cloud Azure Grafana
Transparency: every engagement is quoted clearly before it starts. No opaque packages, no hidden commitments.

Already know what you need?

Or not yet? Either way, 30 minutes is enough to see more clearly.

Book a call