Senior ML/AI Engineer

sedha consulting pte. ltd.; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

SEDHA CONSULTING PTE. LTD.

is seeking a Senior ML/AI Engineer (Senior Consultant) to drive end-to-end ML/AI platform initiatives in Singapore. You will 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 across data ingestion, experimentation, deployment, monitoring, retraining, and retirement.

Qualifications

• Strong Python and software-engineering capability, 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 for distributed data processing and pipeline troubleshooting.
• Experience designing lakehouse/data-lake pipelines with Databricks, Delta Lake and AWS S3.

Responsibilities

• Design and implement reusable platform components and golden paths for experiment tracking and deployment.
• Convert notebooks and prototypes into modular, tested services and endpoints with operational ownership.
• Design and maintain feature engineering pipelines on Databricks and AWS for ML datasets.

Skills

Python

APIs

Packaging

Automated testing

Git

Code review

Debugging

Modular design

Education

Degree in Computer Science, AI, Data Science, Software Engineering, Information Systems or related field

Tools

Databricks

MLflow

Spark/PySpark

SQL

Delta Lake

AWS S3

Terraform

GitLabCI

GitHub Actions

Jenkins

Harness

Job description

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 teamsand 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/AIOpsPlatform Engineering: Design and implementreusable platform components and golden paths for experiment tracking, modelregistry, pipeline orchestration, deployment, monitoring, feedbackand retraining usingDatabricks, MLflow and approved AWS services.
• ProductionEngineering: Convert notebooks and prototypes intomodular, 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 configurationand workload scheduling to improve reliability, runtime and AWS/Databricks costefficiency.
• 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; capturelineage, 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; 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 AIinputs and tool use.
• Recommendation and Search Support: Support model maintenance and retraining, package and dependency updates, newdata ingestion, experimentation, filters, logic changes and performance reporting for SWDA recommendation and search workloads.
• Optimization 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, releaseevidence, 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: 4–7 years of relevant software, data or ML engineering experience, including hands-ondelivery and operation of ML/AI systems beyond prototype stage.

B. Technical & Professional Competencies

Essential (Must-Have)

• Strong Python and software-engineering capability, including APIs, packaging, automated testing, Git, codereview, 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 optimization, partitioning, joins, incremental processing and troubleshooting data pipelines.
• Experience designing and implementing lakehouse or data-lake pipelines using Databricks, Delta Lake and AWSS3, 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 GitLabCI, 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 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 using MSK/Kinesis/Kafka, and cross-account or governed data-sharing patterns.
• Experience productionising generative-AI or agentic systems, 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-challengerdeployment.
• 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.

C. Soft Skills & Communication Attributes

• Strong engineering judgement and a pragmatic mindset, with the ability to distinguish a supportable production service from a demonstration.
• Structured problem-solving across data, model, application and infrastructure layers, including calm and evidence-based incident handling.
• Ability to work across data engineering and ML engineering boundaries, translating model requirements into dependable datasets, features, interfaces and service-level expectations.
• Clear written and verbal communication, including the ability to explain architecture, evaluation evidence, risk, cost and operational trade-offs to technical and management audiences.
• Collaborative workingstyle with data scientists, data engineers, platform engineers, product teams, cybersecurity and governance stakeholders.
• Ability to work independently, document decisions, manage ambiguity and deliver through incremental, testable outcomes.
• Commitment to knowledge transfer, reusable engineering patterns and sustainable in-house capability rather than bespoke solutions.