全职 Senior ML-AI Engineer 工作, 薪水, Sedha Consulting 公司招聘中 - Ricebowl

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.