The posting
At Xtremax, we're looking for a Senior ML/AI Engineer to help build the engineering foundations that make machine learning and AI systems reliable in production. Working across AWS and Databricks, you'll help turn experiments and notebooks into maintainable services, reusable pipelines and operational AI capabilities.
You'll work across the ML lifecycle, from data preparation and feature engineering through experiment tracking, deployment, monitoring, evaluation, retraining and retirement. Using technologies such as Python, PySpark, SQL, Databricks and MLflow, you'll improve the reliability, reproducibility and cost efficiency of AI workloads while establishing reusable engineering patterns for the wider team.
This is a hands-on engineering role that spans software, data and infrastructure. You'll collaborate with data scientists, data engineers, platform engineers, product teams and governance stakeholders to build secure and observable services, improve deployment practices, and support the continued maturity of MLOps and AIOps capabilities.
Responsibilities
- Design and implement reusable MLOps/AIOps components for experiment tracking, model registry, workflow orchestration, deployment, monitoring, feedback and retraining.
- Productionise notebooks and prototypes into tested, modular and containerised batch jobs, APIs, model-serving endpoints and AI services.
- Build reliable data and feature engineering pipelines on Databricks and AWS for structured and unstructured data.
- Optimise Spark and SQL workloads, partitioning, file layouts, cluster configuration and scheduling to improve performance and cloud cost efficiency.
- Automate testing and promotion of code, data, features, prompts, models and infrastructure through CI/CD and continuous-training workflows.
- Implement versioning, lineage, evaluation evidence, approval records, rollback and retirement mechanisms for ML/AI workloads.
- Build system and model monitoring, dashboards, alerts and operational runbooks, and support incident diagnosis and root-cause analysis.
- Develop repeatable model evaluation and regression-testing practices, including champion-challenger comparisons and A/B testing where appropriate.
- Apply cloud security and responsible-AI controls covering access, encryption, secrets, audit logging and safeguards for untrusted AI inputs.
- Support model maintenance, retraining, data ingestion, experimentation and performance reporting for recommendation and search workloads.
- Improve reusable engineering patterns, operational reliability and MLOps maturity, while documenting solutions and transferring knowledge to internal teams.
Requirements
- Degree or diploma in Computer Science, Artificial Intelligence, Data Science, Software Engineering, Information Systems or a related technical discipline.
- Typically 4–7 years of relevant software, data or ML engineering experience, including delivery and operation of ML/AI systems beyond prototype stage.
- Strong Python software engineering skills, including API development, packaging, automated testing, Git and modular design.
- Hands-on experience with Databricks and MLflow for experiment tracking, model registry, orchestration or model serving.
- Strong experience with Spark/PySpark and SQL, including distributed processing, query optimisation and pipeline troubleshooting.
- Experience building lakehouse or data-lake pipelines using Databricks, Delta Lake and AWS S3, including layered data patterns.
- Experience with data modelling, data contracts, metadata, lineage, quality validation and reconciliation.
- Experience with workflow orchestration using Databricks Workflows, AWS Glue, Step Functions, EventBridge, Airflow or equivalent tools.
- Practical AWS data and cloud engineering experience across relevant services, such as S3, Glue, IAM, KMS, Lambda, ECR, ECS/EKS, CloudWatch or Redshift.
- Experience with Docker, infrastructure as code such as Terraform, and CI/CD tools such as GitLab CI, GitHub Actions, Jenkins or Harness.
- Understanding of the ML lifecycle, including data validation, feature engineering, evaluation, deployment, monitoring, drift, retraining and retirement.
- Experience with observability, operational troubleshooting, reproducibility and traceability.
- Working knowledge of cloud security, access management, encryption, secrets management and audit logging.
Preferred
- Experience with Unity Catalog, Delta Live Tables, Lakeflow or feature stores.
- Experience with streaming, change data capture or governed data sharing.
- Experience productionising generative AI or agentic systems, including orchestration, evaluation and guardrails.
- Familiarity with Kubernetes, OpenTelemetry, Prometheus, Grafana or model drift tooling.
- Experience with A/B testing or champion-challenger deployment.
- Experience supporting high-volume or low-latency AI services.
- Familiarity with Singapore public-sector technology environments, security controls and responsible-AI governance.
Benefits
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