ML Engineer

Machine Learning Engineer – AWS Workflow Specialist / ML Ops

We are looking for a Machine Learning Engineer with strong expertise in designing, building, automating, and deploying end‑to‑end ML workflows on AWS. The ideal candidate will have deep experience in operationalising ML solutions, implementing robust CI/CD practices, and ensuring high-quality, production-grade ML pipelines

Key Skills and Experience:

• Only CVs clearly demonstrating hands-on AWS experience will be considered
• Proven experience in designing, developing, and deploying SageMaker Pipelines, including data preparation, model training, evaluation, and model registry integration.
• Ability to operationalise ML pipelines through the full development lifecycle—development, testing, integration testing, CI/CD, and production deployment.
• Experience writing and maintaining unit tests, integration tests, and pipeline validation tests for ML workflows and SageMaker components.
• Strong experience automating ML operations using Airflow DAGs, including dependency management, scheduling, error handling, and operational monitoring. Ability to develop unit tests for Airflow DAGs and validate DAG logic as part of CI/CD workflows.
• Extensive experience with GitLab (or similar) in enterprise environments -covering repository management and governance, Branching strategies (Tagging, forking etc), meerge request workflows ,CI/CD pipeline configuration for ML and data workflows
• Strong understanding of code management practices, versioning, environment isolation, and artifact management.
• Proficient in Python, PySpark, and SQL for developing robust ML pipelines.
• Deep understanding and hands-on experience with AWS services including S3, KMS, Lambda, Secrets Manager, CodeBuild, CodePipeline, SageMaker Pipelines, and SageMaker Endpoints.
• Experience managing secure, scalable cloud environments following enterprise security standards
• Hands on experience in orchestrating complex workflows using Airflow and integrating real-time streaming data from Kafka.