Part-Time Full Stack AI/ML Engineer

I’m building a product that relies on cutting-edge reinforcement learning and I need a hands-on engineer who can take the idea from raw data all the way to a production-ready service. The role is part-time and fully remote, but I’m looking for someone who treats ownership seriously and can commit to regular weekly check-ins. Here’s what the work looks like day-to-day: • Data preprocessing

• design robust ETL pipelines, clean and transform large structured and unstructured datasets, and set up automated data validation. • Model development
• research and implement reinforcement learning algorithms, experiment quickly, tune hyperparameters, and evaluate against clear success metrics. • Integration with existing systems
• wrap trained models behind REST/GraphQL endpoints, containerise (Docker/Kubernetes), and wire everything into my current Python micro-services stack on AWS. Everything is Python-first, so fluency with PyTorch or TensorFlow, pandas, NumPy, and popular RL libraries (Stable-Baselines3, Ray RLlib, or similar) is expected. Familiarity with CI/CD (GitHub Actions), infrastructure-as-code, and basic DevOps will make collaboration smoother. Deliverables I’m expecting:

1. Reproducible training pipeline with documented code.
2. Baseline RL model that reaches the agreed-upon performance benchmark.
3. API or service that exposes inference endpoints and plugs seamlessly into my system.
4. Short deployment guide plus key findings from experiments. I’ll define small milestones so we can iterate rapidly and keep scope under control. If you enjoy end-to-end responsibility and like solving real-world problems with reinforcement learning, I’d love to work together.