Senior ML Platform Engineer – MLOps & Production

jobster private ltd.; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

jobster private ltd. seeks a senior ML platform engineer to design and build scalable MLOps infrastructure on Databricks and AWS.

You will own experiment tracking, model registry, pipelines and governance, turning notebooks into production-grade services with automated testing and cost-aware configurations. You will implement reproducibility, monitoring, and secure data handling while driving CI/CD automation and reliable deployment across environments.

Qualifications

• Education: Degree in Computer Science, Artificial Intelligence, Data Science, Software Engineering, Information Systems or related technical discipline.
• Experience: Relevant software, data or ML engineering experience with end-to-end production ownership.

Responsibilities

• Design 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.
• Convert notebooks and prototypes into modular, tested, containerised batch jobs, APIs, model-serving endpoints or agentic services with clear interfaces and ownership.
• Design, build and maintain feature engineering pipelines on Databricks and AWS for ingesting, transforming, validating and serving data used by ML workloads.
• Optimize Spark and SQL jobs, file layout, partitioning, cluster configuration and scheduling for reliability and cost efficiency.
• Build automated CI/CD/CT workflows for testing and promoting code, data, features, prompts, models and infrastructure across environments with gates.
• Implement versioning, lineage, approvals, deployment history and rollback mechanisms for data, models and environments.
• Create dashboards and alerts for system health, performance, cost and data quality; maintain runbooks and incident response.
• Develop offline/online evaluation, A/B testing and champion-challenger patterns with clear release criteria.
• Apply security, encryption, least-privilege access and governance for responsible AI and data protection.
• Support model maintenance, data ingestion and experimentation for recommendation and search workloads.
• Drive scalability, resilience and cost efficiency; contribute to MLOps maturity roadmap.
• Produce architecture decisions, release evidence and user guidance; enable sustainable capability transfer to teams.

Skills

Python

APIs

Automated testing

Git

Code review

Modular design

Spark/PySpark

SQL

Data modelling

Reproducibility

Education

Degree in Computer Science / Data Science / Software Eng

Tools

Databricks

MLflow

AWS (S3, Glue, IAM, Lambda)

Terraform

Docker

Kubernetes

Airflow

GitLab CI / GitHub Actions

Job description

jobster private ltd. seeks a senior ML platform engineer to design and build scalable MLOps infrastructure on Databricks and AWS.

You will own experiment tracking, model registry, pipelines and governance, turning notebooks into production-grade services with automated testing and cost-aware configurations. You will implement reproducibility, monitoring, and secure data handling while driving CI/CD automation and reliable deployment across environments.