Space Executive Hiring! Full Time ML Engineer in - Ricebowl

We are looking for an ML Engineer to own the operationalisation of AI/ML models — taking models from development into reliable, scalable production systems. This is a hands-on engineering role sitting at the intersection of ML and platform infrastructure. You'll work closely with their data scientists to understand workflows and build the infrastructure that makes deploying, monitoring, and maintaining models systematic and repeatable.

What You'll Do

• Own the deployment and operationalisation of ML models into production — building the infrastructure that takes models from development to live business systems
• Build and maintain ML pipelines using tools like MLflow, Airflow, and similar — covering experiment tracking, model versioning, deployment, and automated retraining
• Implement model monitoring and drift detection to ensure production models stay accurate over time
• Build and maintain cloud infrastructure on AWS, GCP, or Azure using Docker, Kubernetes, and CI/CD pipelines
• Work closely with data scientists to understand their workflow and build platform tooling that enables them to deploy and iterate on models independently
• Write clean, well-structured Python code and contribute to engineering standards across the team

What We're Looking For

• 2–4 years of hands-on experience in ML engineering or a closely related role
• Practical experience deploying ML models into production environments — not just building or training them
• Hands-on experience with ML lifecycle tools — MLflow, Kubeflow, SageMaker, Vertex AI, or similar
• Experience with model monitoring and drift detection in production
• Comfortable with Docker, Kubernetes, and CI/CD pipelines
• Cloud experience on AWS, GCP, or Azure
• Strong Python skills with good software engineering fundamentals
• Experience with workflow orchestration tools — Airflow, Prefect, or similar

Bonus points for:

• Experience with supply chain AI — demand forecasting, inventory optimisation, or similar
• Familiarity with traditional ML models (time-series forecasting, gradient boosted trees, regression) rather than purely GenAI/LLM work
• Infrastructure as code experience (Terraform, Helm)