Senior ML/AI Engineer

Senior ML/AI Engineer (Senior Consultant)

The role combines ML/AI engineering with practical data engineering, working closely with data scientists, data engineers, platform engineers, product teamsand 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 throughdeployment, monitoring,retraining and retirement.

Key Responsibilities

· MLOps/AIOpsPlatform Engineering: Design and implementreusable platform components and golden paths for experiment tracking, modelregistry, pipeline orchestration, deployment, monitoring, feedbackand retraining usingDatabricks, MLflow and approved AWS services.

· ProductionEngineering: Convert notebooks and prototypes intomodular, tested, containerized and maintainable batch jobs, APIs, model-serving endpoints or agentic services, with clear interfaces and operational ownership.

· Feature Engineering Pipeline: Design, build and maintainreliable 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: Optimize Spark and SQL jobs, file layout, partitioning, cluster configurationand workload scheduling to improve reliability, runtime and AWS/Databricks costefficiency.

· 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; capturelineage, 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 utilization, 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 AIinputs and tool use.

· Recommendation and Search Support: Support model maintenance and retraining, package and dependency updates, newdata ingestion, experimentation, filters, logic changes and performance reporting for SWDA recommendation and search workloads.

· Optimization 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, releaseevidence, 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: 4–7 years of relevant software, data or ML engineering experience, including hands-ondelivery and operation of ML/AI systems beyond prototype stage.

B. Technical & Professional Competencies

Essential (Must-Have)

· Strong Python and software-engineering capability, including APIs, packaging, automated testing, Git, codereview, 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 optimization, partitioning, joins, incremental processing and troubleshooting data pipelines.

· Experience designing and implementing lakehouse or data-lake pipelines using Databricks, Delta Lake and AWSS3, 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 GitLabCI, 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 diagnosisand 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, modeldrift tooling, A/B testing or champion-challengerdeployment.

· 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 workingstyle 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.