Senior ML/AI Engineer

Your tasks

• 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

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.

Other

Role: Senior ML/AI Engineer

JD Working Hours Mon–Fri, 8:30am–6:00pm