**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** .