Senior ML/AI Engineer

Tap Growth ai; Singapore; On-site; SGD 90,000 - 140,000; Full time

Job summary

Tap Growth ai is hiring a Senior ML/AI Engineer in Singapore. The role focuses on designing, building and operating production ML/AI systems using Databricks, MLflow and AWS. You will own end-to-end delivery from data to deployment.

The ideal candidate has 4–7 years in ML engineering, strong Python, Spark, SQL skills, and experience with lakehouse patterns, orchestration tools and robust CI/CD practices.

Qualifications

• Degree in Computer Science, AI, Data Science, Software Engineering or related field.
• 4–7 years in software, data or ML engineering with production ownership.

Responsibilities

• Design and implement MLOps platforms for experiment tracking and model serving.
• Build containerised batch jobs and APIs with clear interfaces.
• Develop feature pipelines on Databricks and AWS for ML workloads.
• Optimize Spark/SQL workloads for performance and cost.
• Create CI/CD/CT automation for code, data, features and models.
• Ensure reproducibility with versioning, lineage and governance.

Skills

Python

Spark

SQL

Docker

Terraform

CI/CD

Git

Education

Bachelor's degree in CS/AI/DS

Tools

Databricks

MLflow

Airflow

Terraform

AWS

Job description

We're Hiring: Senior ML/AI Engineer!

We are seeking an experienced and innovative Senior ML/AI Engineer to join our dynamic team in Singapore. The ideal candidate will leverage advanced machine learning and artificial intelligence techniques to design, develop, and deploy robust solutions that drive business impact.

Location: Singapore, Singapore

Work Mode: Work from Office

Role: Senior ML/AI Engineer

JD:

Working Hours Mon–Fri, 8:30am–6:00pm

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: 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-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 lake house 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, Event Bridge, 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, Event Bridge, 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, 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.