Machine Learning Engineer

We are looking for a Machine Learning Engineer to design, train, and operationalize predictive and analytical ML systems at scale on a 12-month hybrid/remote contract (with option to renew).

As a Machine Learning Engineer, you will translate data strategies into production-grade predictive models. You will take complete ownership of the ML lifecycle—building automated training pipelines, engineering feature store components, and establishing continuous monitoring to prevent model drift.

Must-Haves:

• ML Modeling & Frameworks: Predictive and analytical model design, performance tuning, hyperparameter optimization, and extensive model evaluation.
• Pipelines & Feature Stores: End-to-end training pipeline development and feature store component creation.
• Model Deployment & Operations: Production model deployment, model versioning, continuous monitoring, and drift detection.
• Data Integration: Feature engineering and collaborating with data engineers to optimize datasets for training and inference.

Nice-to-Haves:

• MLOps integration with CI/CD pipelines, automated retraining schedules, and Infrastructure-as-Code (IaC).
• Hands-on experience with vector stores, GenAI applications, and RAG architectures.

Key Responsibilities

• Build Production Models: Design, optimize, and deploy high-performance predictive ML models and scalable training pipelines.
• Engineer Features & Datasets: Perform deep feature engineering and partner with data teams to streamline dataset preparation for training and inference.
• Manage Model Lifecycles: Oversee versioning, deployment registries, and feature store infrastructure.
• Monitor Performance & Drift: Establish real-time tracking dashboards, conduct performance evaluations, and manage drift detection.

Why Join Us?

• Production Focus: Move past experimental notebooks to deploy models directly into high-impact live environments.
• Modern ML Ecosystem: Work hands-on with dedicated feature stores, automated pipeline tooling, and modern MLOps practices.
• Hybrid / Remote Flexibility: Enjoy strong engineering autonomy within a flexible, remote-friendly workplace