ML Ops Engineer

Role Purpose Plays a critical role in delivering AI‑enabled risk assessment and decision support capabilities for the Guardian / ITS platform, translating analytical concepts into production‑ready, governable ML solutions within a high‑security government environment. [office.com]

Requirements:
Key Responsibilities 1) Machine Learning Delivery - Design, develop, and validate ML models supporting risk scoring, profiling, and pattern detection. - Perform feature engineering, experimentation, and model evaluation on operational datasets. - Translate analytical hypotheses into deployable ML outcomes. 2) AI POC & Innovation - Contribute to AI Proof‑of‑Concept initiatives to validate feasibility, value, and scalability. - Rapidly prototype ML approaches and document findings, limitations, and recommendations. - Support transition of validated POCs toward production pathways. 3) Production & MLOps Alignment - Collaborate with engineering and platform teams to prepare models for controlled deployment. - Support model versioning, reproducibility, retraining considerations, and monitoring indicators. - Contribute to ML runbooks and operational readiness artefacts. 4) Architecture & Design Collaboration - Participate in solution architecture and design reviews from an ML perspective. - Ensure ML components align with system architecture, data contracts, and integration boundaries. - Advise on ML constraints, trade‑offs, and dependencies in system design decisions. 5) Responsible & Secure AI - Apply responsible AI principles including explainability, traceability, and bias awareness. - Ensure ML solutions comply with data protection, security, and governance requirements. - Support documentation required for audits, reviews, and regulatory assurance. 6) Stakeholder & Team Engagement - Provide clear updates on ML progress, risks, and outcomes to project stakeholders. - Collaborate cross‑functionally with data engineers, architects, and delivery leads. - Contribute to knowledge sharing and capability uplift within the ML / AI team. 7) Role Context - Embedded within the Machine Learning / AI team supporting Guardian / ITS. - Works closely with Architecture, Data Engineering, and Platform teams. - Direct contributor to the programme's AI roadmap and delivery outcomes