The posting
Key Responsibilities • MLOps/AIOps Platform Engineering: Design and implement reusable platform components and golden paths for experiment tracking, model registry, pipeline orchestration, deployment, monitoring, feedback and retraining using Databricks, MLflow and approved AWS services. • Production Engineering: Convert notebooks and prototypes into modular, tested, containerised and maintainable batch jobs, APIs, model-serving endpoints or agentic services, with clear interfaces and operational ownership. • Feature Engineering Pipeline: Design, build and maintain reliable feature engineering pipelines on Databricks and AWS for ingesting, transforming, validating and serving structured and unstructured data used by ML/AI workloads. • Performance and Cost Engineering: Optimise Spark and SQL jobs, file layout, partitioning, cluster configuration and workload scheduling to improve reliability, runtime and AWS/Databricks cost efficiency. • CI/CD/CT Automation: Build automated workflows for testing and promoting code, data, features, prompts, models and infrastructure across environments, including quality, security, evaluation and human approval gates. • Reproducibility and Governance: Implement source, data, model, prompt and environment versioning; capture lineage, evaluation evidence, approvals and deployment history; and provide rollback and retirement mechanisms. • Monitoring and Reliability: Implement dashboards and alerts for system health, latency, throughput, errors, resource utilisation, cost, data quality, drift and model performance; maintain runbooks and support incident response and root-cause analysis. • Model Evaluation and Experimentation: Develop repeatable offline and online evaluation, regression testing, champion-challenger comparison and A/B testing patterns, with agreed release and rollback criteria. • Security and Responsible AI: Apply least-privilege IAM, encryption, secrets management, network and environment controls, vulnerability scanning, audit logging, sensitive-data controls and safeguards for untrusted AI inputs and tool use. • Recommendation and Search Support: Support model maintenance and retraining, package and dependency updates, new data ingestion, experimentation, filters, logic changes and performance reporting for recommendation and search workloads. • Optimisation and Continuous Improvement: Improve scalability, resilience, deployment frequency and cost efficiency; reduce manual steps and duplicated patterns; and contribute to the team’s MLOps maturity roadmap. • Documentation and Capability Transfer: Produce architecture decisions, model cards, release evidence, operational runbooks and user guidance, and coach DSAD teams so supported solutions can be operated sustainably.
Requirements & Qualifications A. Educational Qualification & Experience Education: Degree in Computer Science, Artificial Intelligence, Data Science, Software Engineering, Information Systems or a related technical discipline. Experience: Relevant software, data or ML engineering experience, including hands-on delivery and operation of ML/AI systems beyond prototype stage. Candidates with fewer years but strong evidence of end-to-end production ownership may also be considered.
B. Technical & Professional Competencies Essential (Must-Have) • Strong Python and software-engineering capability, including APIs, packaging, automated testing, Git, code review, debugging and modular design. • Hands-on experience with Databricks and MLflow for experiment tracking, model registry, workflow orchestration or model serving. • Strong hands-on experience with Spark/PySpark and SQL, including distributed data processing, query optimisation, partitioning, joins, incremental processing and troubleshooting data pipelines. • Experience designing and implementing lakehouse or data-lake pipelines using Databricks, Delta Lake and AWS S3, including Bronze, Silver and Gold data-layer patterns. • Experience with data modelling, schema design, data contracts, metadata, lineage, data-quality validation and reconciliation for production analytics and ML datasets. • Experience orchestrating batch or streaming workflows using Databricks Workflows, AWS Glue, Step Functions, EventBridge, Apache Airflow or an equivalent enterprise orchestration tool. • Hands-on AWS data and cloud engineering experience with a relevant mix of S3, Glue, Lake Formation, IAM, KMS, ECR, ECS/EKS, Lambda, Step Functions, EventBridge, CloudWatch, API Gateway, Secrets Manager, Redshift, MSK/Kinesis or equivalent approved services. • Experience with Docker, infrastructure as code such as Terraform, and CI/CD platforms such as GitLab CI, GitHub Actions, Jenkins or Harness. • Understanding of ML lifecycle controls covering data validation, feature engineering, training, evaluation, deployment, monitoring, drift, retraining and retirement. • Ability to implement reproducibility and traceability across code, datasets, features, parameters, environments, prompts and model artefacts. • Experience implementing system and model observability, alerts, runbooks, incident diagnosis and rollback or recovery procedures. • Working knowledge of cloud security, least-privilege access, encryption, secrets management, vulnerability management and audit logging.
Preferred (Advantageous) • Experience with Databricks Unity Catalog, Delta Live Tables or Lakeflow, feature engineering/feature stores, change data capture, event streaming using MSK/Kinesis/Kafka, and cross-account or governed data-sharing patterns. • Experience productionising generative-AI or agentic systems, including orchestration, state, prompt/version management, evaluation, controlled tool use, guardrails and human approval. • Experience with Kubernetes, OpenTelemetry, Prometheus, Grafana, model drift tooling, A/B testing or champion challenger deployment. • Experience supporting high-volume, low-latency or public-facing AI services and optimising cloud inference and training costs. • Experience in Singapore public-sector environments, including VAPT, IM8-aligned controls, data classification, system assurance or responsible-AI governance.
C. Soft Skills & Communication Attributes • Strong engineering judgement and a pragmatic mindset, with the ability to distinguish a supportable production service from a demonstration. • Structured problem-solving across data, model, application and infrastructure layers, including calm and evidence-based incident handling. • Ability to work across data engineering and ML engineering boundaries, translating model requirements into dependable datasets, features, interfaces and service-level expectations. • Clear written and verbal communication, including the ability to explain architecture, evaluation evidence, risk, cost and operational trade-offs to technical and management audiences. • Collaborative working style with data scientists, data engineers, platform engineers, product teams, cybersecurity and governance stakeholders. • Ability to work independently, document decisions, manage ambiguity and deliver through incremental, testable outcomes. • Commitment to knowledge transfer, reusable engineering patterns and sustainable in-house capability rather than bespoke solutions.
