Purpose of the role
To implement data quality process and procedures, ensuring that data is reliable and trustworthy, then extract actionable insights from it to help the organization improve its operation, and optimise resources.
Accountabilities
- Investigation and analysis of data issues related to quality, lineage, controls, and authoritative source identification.
- Execution of data cleansing and transformation tasks to prepare data for analysis.
- Designing and building data pipelines to automate data movement and processing.
- Development and application of advanced analytical techniques, including machine learning and AI, to solve complex business problems.
- Documentation of data quality findings and recommendations for improvement.
Purpose of the role:
To design, implement, and maintain robust MLOps frameworks that streamline the deployment, monitoring, and lifecycle management of AI and Generative AI models, ensuring efficient and reliable production operations on AWS.
Responsibilities of the role:
- Build and optimize scalable, secure, and cost-effective AWS-based infrastructure for ML/GenAI workloads
- Develop automated workflows for data ingestion, model training, testing, deployment, and monitoring using tools like AWS SageMaker, Step Functions, and Lambda
- Implement data quality checks, lineage tracking, and compliance standards for curated datasets
- Integrate ML pipelines with DevOps practices, ensuring seamless collaboration between data science and engineering teams
- Deploy monitoring solutions for model performance, drift detection, and system health using AWS CloudWatch and custom dashboards
- Ensure adherence to security best practices and governance requirements for AI deployments
- Work closely with Data Scientists to operationalize models and optimize deployment strategies
Technical skills required for this role include:
- Experience in Programming & Automation: Python, Bash, SQL.
- Worked in MLOps Tools: MLflow, Kubeflow, AWS SageMaker Pipelines.
- Cloud Platforms: AWS (SageMaker, Bedrock, Lambda, Step Functions, CloudWatch)
- DevOps: CI/CD (GitHub Actions, Jenkins), Docker, Kubernetes
- Data Management: Data curation, governance, and ETL processes.
The ML Ops Engineer role focuses on building and managing automated pipelines, AWS-based architectures, and monitoring frameworks to enable efficient deployment and lifecycle management of AI and Generative AI models in production environments.
This role requires a flexible working approach, ensuring availability during select hours that overlap with US-based partners and stakeholders.
This role is based out of Noida.