ML/AI Engineer (Public Sector)
First seen by Alion on Oct 28, 2026.
- 2-year contract, renewable
- Hybrid work arrangement
- Government project
Role Overview
The ML/AI Engineer is part of the Data Platform and Architecture Team. You will productionise, operate and improve machine-learning and AI workloads on the SWDA data platform using AWS and Databricks.
This role combines ML/AI engineering with practical data engineering. Working closely with data scientists, data engineers, platform engineers, product teams and governance stakeholders, you will build reliable data and feature pipelines, prepare production-ready datasets, and establish reusable MLOps and AIOps capabilities. Your work will support secure, observable and cost-effective AI services throughout their lifecycle, from data ingestion and experimentation to deployment, monitoring, retraining and retirement.
Key Responsibilities
MLOps and AIOps platform engineering: Design and implement reusable platform components and standard workflows for experiment tracking, model registry, pipeline orchestration, deployment, monitoring, feedback and retraining using Databricks, MLflow and approved AWS services.
Production engineering: Turn 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 pipelines: Build and maintain reliable pipelines on Databricks and AWS to ingest, transform, validate and serve structured and unstructured data for ML/AI workloads.
Performance and cost engineering: Optimise Spark and SQL jobs, file layouts, partitioning, cluster configurations and workload schedules to improve reliability and runtime, while managing AWS and Databricks costs.
CI/CD/CT automation: Build automated workflows to test and promote code, data, features, prompts, models and infrastructure across environments. Include quality, security and evaluation checks, as well as human approval gates.
Reproducibility and governance: Implement versioning for source code, data, models, prompts and environments. Capture lineage, evaluation evidence, approvals and deployment history, and provide rollback and retirement mechanisms.
Monitoring and reliability: Build dashboards and alerts for system health, latency, throughput, errors, resource use, cost, data quality, drift and model performance. Maintain runbooks and support incident response and root-cause analysis.
Evaluation and experimentation: Develop repeatable offline and online evaluation, regression testing, champion–challenger comparisons 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 maintenance and retraining, package and dependency updates, new data ingestion, experimentation, filter and logic changes, and performance reporting for SWDA recommendation and search workloads.
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. Coach DSAD teams so they can operate supported solutions sustainably.
Requirements and Qualifications
Education and ExperienceEducation: Degree in Computer Science, Artificial Intelligence, Data Science, Software Engineering, Information Systems or a related technical discipline.
Experience: Typically 4–7 years in software, data or ML engineering, including hands-on delivery and operation of ML/AI systems beyond the prototype stage. Candidates with fewer years of experience may also be considered if they can demonstrate strong end-to-end production ownership.
Strong Python and software engineering skills, 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 pipeline troubleshooting.
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 services, such as 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 ensure 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.
Databricks Unity Catalog, Delta Live Tables or Lakeflow; feature engineering or feature stores; change data capture; event streaming with MSK, Kinesis or Kafka; and cross-account or governed data-sharing patterns.
Productionising generative AI or agentic systems, including orchestration, state, prompt and version management, evaluation, controlled tool use, guardrails and human approval.
Kubernetes, OpenTelemetry, Prometheus, Grafana, model drift tooling, A/B testing or champion–challenger deployment.
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.
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, evidence-based incident handling.
Ability to work across data engineering and ML engineering, 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 approach when working 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.
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Skills
Candidate Screening, Technical Skills, DevOps, Artificial Intelligence, Build Prototypes, Databricks, Computer Science, Joining Formalities, Workflow Orchestration, Feature Engineering, Cost Engineering, Structured data analysis, MLflow, Education, Programming, Production Engineering