Senior ML /AI
Your tasks
operating and improving machine-learning and AI workloads on the SWDA data platform using AWS and Databricks.
Requirements
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-toend
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.
Other
Work location- Paya Lebar The ML/AI Engineer sits within the DSAD Platform and Architecture Team and is responsible for productionising,