Senior ML/AI Engineer

**Senior ML/AI Engineer (Senior Consultant)** The role combines ML/AI engineering with practical data engineering, working closely with data scientists, data engineers, platform engineers, product teams and governance stakeholders to build reliable data and feature pipelines, curate production-ready datasets, and establish reusable MLOps/AIOps capabilities that support secure, observable and cost-effective AI services from data ingestion and experimentation throughdeployment, monitoring, retraining and retirement. ***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, containerized and maintainable batch jobs, APIs, model-serving endpoints or agentic services,with clear interfaces and operational ownership. * **Feature Engineering Pipeline:** Design, build and maintainreliable 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:** Optimize 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 automatedworkflows for testingand 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 utilization, cost, data quality,drift and model performance; maintainrunbooks and supportincident 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, packageand dependency updates, new data ingestion, experimentation, filters, logic changes and performance reporting for recommendation and search workloads. * **Optimization and Continuous Improvement:** Improve scalability, resilience, deployment frequency and cost efficiency; reducemanual steps and duplicated patterns; and contribute to the team’sMLOps maturity roadmap. * **Documentation and Capability Transfer:** Produce architecture decisions, model cards,release evidence, operational runbooks and user guidance, and coach teams so supported solutions can be operated sustainably. ***Requirements & Qualifications*** *EducationalQualification & Experience* **Education:** Degree in Computer Science,Artificial Intelligence, Data Science, SoftwareEngineering, Information Systems or a related technical discipline. **Experience:** 4–7 years of relevantsoftware, data or ML engineering experience, including hands-ondelivery and operation of ML/AI systems beyondprototype stage. Candidates with fewer years but strongevidence of end-to-end production ownership may also be considered. *Technical & Professional Competencies* Essential (Must-Have) * Strong Pythonand 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, workfloworchestration or model serving. * Strong hands-onexperience with Spark/PySpark and SQL, includingdistributed data processing, query optimization, partitioning, joins, incremental processing and troubleshooting data pipelines. * Experience designingand implementing lakehouse or data-lake pipelinesusing 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 streamingworkflows 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 platformssuch as GitLabCI, GitHub Actions, Jenkins or Harness. * Understanding of ML lifecyclecontrols covering data validation, featureengineering, 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 diagnosisand rollback or recovery procedures. * Working knowledge of cloud security, least-privilege access, encryption, secrets management, vulnerability management and audit logging. Preferred (Advantageous) * Experience with Databricks Unity Catalog, Delta Live Tables or Lakeflow, feature engineering/feature stores, change data capture,event streaming usingMSK/Kinesis/Kafka, and cross-account or governed data-sharing patterns. * Experience productionising generative-AI or agenticsystems, including orchestration, state, prompt/version management, evaluation, controlled tool use, guardrails and human approval. * Experience with Kubernetes, OpenTelemetry, Prometheus, Grafana, modeldrift tooling, A/B testing or champion-challenger deployment. * Experience supporting high-volume, low-latency or public-facing AI services and optimising cloud inference and training costs. * Experience in Singapore public-sector environments, including VAPT, IM8-aligned controls,data classification, system assurance or responsible-AI governance.