Senior ML/AI Engineer

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.

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 lakehouse 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, 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 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

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