M01 - ML/AI Engineer (DPA)

Role Overview

The ML/AI Engineer sits within the data platform and architecture team and is responsible for productionising, operating and improving machine-learning and AI workloads on the enterprise data platform using AWS and Databricks. The role combines ML/AI engineering with practical data engineering, working closely with data scientists, data engineers, platform engineers, product teams and governance stakeholders to build reliable data and feature pipelines, curate production-ready datasets, and establish reusable MLOps/AIOps capabilities that support secure, observable and cost-effective AI services from data ingestion and experimentation through deployment, monitoring, retraining and retirement.

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 internal teams so supported solutions can be operated sustainably.

Education & Experience

• Education: Degree in Computer Science, Artificial Intelligence, Data Science, Software Engineering, Information Systems or a related technical discipline.
• Experience: Typically 4–7 years of 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.

Essential Requirements

• 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 Qualifications

• 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.

Soft Skills

• 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.

Originally posted on Himalayas