Senior ML/AI Engineer - #1733

JOBSTER PRIVATE LTD.; Singapore; On-site; SGD 120,000 - 240,000; Full time

Job summary

JOBSTER PRIVATE LTD. seeks a senior MLOps Platform Engineer to design and implement reusable platform components for experiment tracking, model registry, and deployment on Databricks and AWS.

You will convert notebooks into modular, containerised production services, optimise data pipelines, and build robust CI/CD workflows with governance and cost awareness. Strong Python and Spark skills are essential.

Qualifications

• Strong Python and software-engineering capability with APIs, packaging and testing.
• Hands-on with Databricks and MLflow for experiment tracking, registry and orchestration.
• Strong Spark/PySpark and SQL experience for distributed data processing and pipelines.
• Experience with Databricks Lakehouse, Delta Lake and AWS S3 patterns.
• Data modeling, schema design, data contracts and lineage in production analytics.

Responsibilities

• MLOps/AI Platform Engineering: design reusable components for experiment tracking, model registry and deployment.
• Production Engineering: convert notebooks to modular, containerised batch jobs and APIs with clear interfaces.
• Feature Engineering: build pipelines on Databricks and AWS for data serving.
• Performance and Cost Engineering: optimise Spark/SQL and cost across AWS/Databricks.
• CI/CD/CT Automation: automate testing, promotion and governance across environments.
• Reproducibility and Governance: track lineage, approvals and deployment history.
• Monitoring and Reliability: dashboards for system health, latency and data quality.
• Model Evaluation and Experimentation: offline/online evaluation and A/B testing patterns.

Skills

Python

APIs & Testing

Git & Code Review

SQL

Distributed Data Processing

Data Modeling

MLOps Concepts

Education

Bachelor's degree in Computer Science or related

Tools

Databricks

MLflow

Spark/PySpark

AWS (S3, Glue, Lambda)

Docker

Terraform

Airflow

CI/CD (GitHub Actions/GitLab CI)

Job description

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 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: 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.

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.


• 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, en