Full-Stack AI/ML Engineer, CST Hours
Budget: $250 – $750 USD
I’m looking for a versatile full-stack AI/ML engineer who can join me Monday-Friday, 9 AM-5 PM CST, and communicate clearly in English throughout the day. This role centres on a commercial application that spans Healthcare, Finance, and Retail, so you’ll be switching contexts and datasets often.
Day-to-day you’ll be:
• Exploring raw data, cleaning it, and shaping robust feature sets
• Designing and training predictive models and optimisation algorithms
• Deploying, monitoring, and continually improving those models in production
• Owning the backend APIs, batch pipelines, and any lightweight front-end hooks needed to surface the insights
We work primarily in Python with TensorFlow/PyTorch, SQL, and a cloud stack (AWS + Docker/Kubernetes), though I’m open to comparable tools if you can justify the choice.
Deliverables I expect:
• Production-ready models that hit agreed accuracy / latency targets
• Reusable, well-documented code and CI/CD pipelines
• Clear weekly progress updates and a hand-off package that another engineer can pick up without friction
If you thrive on end-to-end ownership—data wrangling to live deployment—while keeping business goals front and centre, let’s talk.
Day-to-day you’ll be:
• Exploring raw data, cleaning it, and shaping robust feature sets
• Designing and training predictive models and optimisation algorithms
• Deploying, monitoring, and continually improving those models in production
• Owning the backend APIs, batch pipelines, and any lightweight front-end hooks needed to surface the insights
We work primarily in Python with TensorFlow/PyTorch, SQL, and a cloud stack (AWS + Docker/Kubernetes), though I’m open to comparable tools if you can justify the choice.
Deliverables I expect:
• Production-ready models that hit agreed accuracy / latency targets
• Reusable, well-documented code and CI/CD pipelines
• Clear weekly progress updates and a hand-off package that another engineer can pick up without friction
If you thrive on end-to-end ownership—data wrangling to live deployment—while keeping business goals front and centre, let’s talk.