Fractional CTO for ML Startup
Budget: $25 – $50 CAD
I’m at the idea stage of a machine-learning–driven application and need a seasoned, CTO-level partner who can own the technical side from day one.
What I’m looking for
• Shape the technical strategy and high-level architecture, making sure it can scale as the product matures.
• Stay hands-on: write the first lines of production-ready code, set up repos, CI/CD, and deploy an initial proof-of-concept or MVP.
• Guide early hiring decisions once we’re ready to build a wider engineering team.
Tech stack
I’m open to your recommendations. Whether that’s Python with TensorFlow/PyTorch, a cloud-native approach on AWS, GCP, or Azure, or even low-latency options in another language, I want to leverage whichever tools best fit the problem and timeline.
Deliverables (first 4–6 weeks)
1. A concise technical roadmap outlining phases, risks, and resource needs.
2. A documented architecture diagram and decision log.
3. A functional prototype or skeleton service that demonstrates the core ML workflow (data ingest, training, and inference pipeline).
4. Deployment scripts or containerization for easy hand-off and future scaling.
If you thrive in zero-to-one environments, enjoy shaping product strategy with founders, and can both think big and code small, let’s talk.
What I’m looking for
• Shape the technical strategy and high-level architecture, making sure it can scale as the product matures.
• Stay hands-on: write the first lines of production-ready code, set up repos, CI/CD, and deploy an initial proof-of-concept or MVP.
• Guide early hiring decisions once we’re ready to build a wider engineering team.
Tech stack
I’m open to your recommendations. Whether that’s Python with TensorFlow/PyTorch, a cloud-native approach on AWS, GCP, or Azure, or even low-latency options in another language, I want to leverage whichever tools best fit the problem and timeline.
Deliverables (first 4–6 weeks)
1. A concise technical roadmap outlining phases, risks, and resource needs.
2. A documented architecture diagram and decision log.
3. A functional prototype or skeleton service that demonstrates the core ML workflow (data ingest, training, and inference pipeline).
4. Deployment scripts or containerization for easy hand-off and future scaling.
If you thrive in zero-to-one environments, enjoy shaping product strategy with founders, and can both think big and code small, let’s talk.