Job Title: Lead Machine Learning Engineer (SageMaker, MLOps, Explainability)
We are seeking an experienced Lead Machine Learning Engineer to design, build, and productionise machine learning models for our innovative matching platform. You will work across the entire ML lifecycle, from feature engineering to deployment automation, ensuring the optimisation and explainability of inference processes. Collaborating closely with data scientists and product teams, your role will focus on enhancing MLOps practices, ensuring high standards of security, performance, and compliance.
Responsibilities
* Build and maintain scalable feature pipelines within data lakehouse architectures.
* Develop fallback feature flows and implement robust data quality checks.
* Develop ranking, scoring, and entity-similarity models for the matching platform.
* Use modern ML model frameworks such as PyTorch, TensorFlow, or XGBoost.
* Apply SHAP or similar techniques to generate interpretable model explanations.
* Build and maintain training, processing, and inference pipelines using AWS SageMaker.
* Deploy and optimise low-latency, real-time inference endpoints.
* Implement feature drift and concept drift monitoring.
* Apply procedures for data handling, encryption, PII minimisation, and auditability.
* Conduct validation of models using golden datasets and baseline tests.
Essential Skills
* Strong experience delivering production-grade ML systems.
* Proficiency with AWS SageMaker, including training jobs and Model Registry.
* Excellent skills with ML models like PyTorch, TensorFlow, or XGBoost.
* Hands‑on experience with model explainability tools such as SHAP.
* Understanding of low‑latency, real‑time inference patterns.
* Experience in drift detection, monitoring, and telemetry.
* Working knowledge of ML governance and secure ML practices.
* Strong understanding of MLOps, CI/CD, and automation for ML workflows.
Additional Skills & Qualifications
* Experience with feature stores or Lakehouse data architectures.
* Previous experience with ranking, matching, or similarity models.
* Familiarity with cross‑account AWS IAM patterns.
* Bachelor's degree in a STEM subject such as mathematics, physics, engineering, or computer science.
Why Work Here?
Join a forward‑thinking company focused on innovation and excellence in machine learning. We provide a collaborative environment where your contributions directly impact the development of cutting‑edge technology. Enjoy opportunities for professional growth and be part of a team dedicated to pioneering advancements in AI/ML.
Work Environment
Work in a dynamic and collaborative environment leveraging state‑of‑the‑art technologies. You will have access to modern tools and resources, including AWS SageMaker and various ML frameworks. Our flexible work culture promotes work‑life balance and encourages continuous learning and development.
Location
London, UK
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