Roles and Responsibilities

• Design, develop, and optimize complex, scalable data pipelines to support machine learning workflows using engineering best practices.
• Build and maintain robust MLOps pipelines to enable automated deployment, monitoring, and lifecycle management of AI/ML models in production.
• Integrate and manage end-to-end model and data pipelines, proactively identifying data quality issues and documenting assumptions and transformations.
• Partner closely with data scientists to validate model-ready datasets and ensure thorough and accurate documentation of features and data lineage.
• Conduct in-depth exploratory data analysis on raw datasets, incorporating business context to support and guide model development.
• Investigate data anomalies and perform root cause analysis during early-stage exploration or when addressing data and model issues.
• Work with internal stakeholders to understand business processes and translate them into scalable, production-grade analytical solutions.
• Develop and maintain model monitoring scripts, handle alerts, investigate issues, and coordinate resolution efforts in a timely manner.

Qualifications

• Bachelor's or Master’s degree in Computer Science, Data Engineering, Machine Learning, or a related field.
• Strong experience in designing and implementing data pipelines using technologies like Python, SQL, Spark, or Airflow.
• Solid understanding of MLOps tools and frameworks (e.g., MLflow, Kubeflow, SageMaker, Vertex AI).
• Familiarity with containerization and orchestration tools like Docker and Kubernetes.
• Experience working with cloud platforms (AWS, GCP, or Azure).
• Excellent collaboration and communication skills with the ability to work cross-functionally.
• Ability to debug complex systems and handle data-related issues effectively.

Preferred Skills

• Experience in CI/CD pipelines for ML deployments.
• Familiarity with model monitoring and drift detection techniques.
• Background in data governance, lineage, and compliance practices.
• Understanding of version control systems (e.g., Git) and agile development processes.

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