Manager, ML Engineer (Data Science), AI Scaling and Transformation, Engineering, AI & Data, Technology & Transformation

Overview As an ML Engineer in Deloittes AI Scaling and Transformation team, you design, build and scale AI/ML solutions for public safety and security clients in regulated environments. You own end-to-end ML workstreams and translate business needs into robust ML capabilities.

You will deploy production-ready models, implement MLOps, and ensure responsible AI governance, working with cross-functional teams to deliver impactful outcomes. Pay / Benefits hybrid working policy flexible working arrangements career progression and world-class development wellbeing support and inclusive culture supportive and collaborative environment Responsibilities Own ML and data science workstreams aligned to client priorities and quality standards Translate operational challenges from stakeholders into practical ML solution designs Design, build, test and deploy scalable ML models and data pipelines Operationalise ML solutions with MLOps: monitoring, versioning, CI/CD, testing, retraining Apply responsible AI, explainability, security, privacy, and governance in development lifecycle Collaborate with architects, data engineers, data scientists and delivery leads to integrate ML into broader tech landscape Manage stakeholders and communicate progress, risks, and recommendations clearly Key requirements Degree or equivalent experience in Computer Science, Data Science, Mathematics, Statistics, AI, ML or related discipline Hands-on ML/data science solution delivery in consulting, public sector or tech delivery Experience owning technical deliverables and coordinating multidisciplinary teams Ability to translate requirements into practical recommendations for technical and business stakeholders Experience in secure, regulated environments; eligible for security clearance if needed Strong Python or other data science language proficiency Practical experience with ML frameworks such as scikit-learn, XGBoost, PyTorch, TensorFlow End-to-end ML lifecycle experience: data prep, feature engineering, model development, deployment, monitoring, optimization MLOps experience: CI/CD, model versioning, automated testing, retraining, monitoring Experience with cloud platforms and data science environments (Azure, AWS, GCP, Databricks) Strong communication and stakeholder management skills Strong communication and stakeholder management Problem-solving能力 Ability to explain complex modelling to technical and non-technical audiences Python or equivalent scikit-learn XGBoost

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