Lead ML Engineer (SageMaker) in EC1
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Job Description
Job Title: Lead Machine Learning Engineer (SageMaker, MLOps, Explainability)\nJob DescriptionWe 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.\n\nResponsibilities\nBuild and maintain scalable feature pipelines within data lakehouse architectures.\nDevelop fallback feature flows and implement robust data quality checks.\nDevelop ranking, scoring, and entity-similarity models for the matching platform.\nUse modern ML model frameworks such as PyTorch, TensorFlow, or XGBoost.\nApply SHAP or similar techniques to generate interpretable model explanations.\nBuild and maintain training, processing, and inference pipelines using AWS SageMaker.\nDeploy and optimise low-latency, real-time inference endpoints.\nImplement feature drift and concept drift monitoring.\nApply procedures for data handling, encryption, PII minimisation, and auditability.\nConduct validation of models using golden datasets and baseline tests.Essential Skills\nStrong experience delivering production-grade ML systems.\nProficiency with AWS SageMaker, including training jobs and Model Registry.\nExcellent skills with ML models like PyTorch, TensorFlow, or XGBoost.\nHands-on experience with model explainability tools such as SHAP.\nUnderstanding of low-latency, real-time inference patterns.\nexperience in drift detection, monitoring, and telemetry.\nWorking knowledge of ML governance and secure ML practices.\nStrong understanding of MLOps, CI/CD, and automation for ML workflows.Additional Skills & Qualifications\nexperience with feature stores or Lakehouse data architectures.\nPrevious experience with ranking, matching, or similarity models.\nFamiliarity with cross-account AWS IAM patterns.\nBachelor'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.\n\nWork EnvironmentWork 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.\n\nLocation\n\n London, UK\n\nTrading as TEKsystems. Allegis Group Limited, Maxis 2, Western Road, Bracknell, RG12 1RT, United Kingdom. No.
(phone number removed). Allegis Group Limited operates as an Employment Business and Employment Agency as set out in the Conduct of Employment Agencies and Employment Businesses Regulations 2003. TEKsystems is a company within the Allegis Group network of companies (collectively referred to as "Allegis Group").
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