ML Ops Engineer

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

Singapore

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