MLOps Engineer

About 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. This 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.

Key Responsibilities

• 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
• 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.
• Consult on complex issues; providing advice to People Leaders to support the resolution of escalated issues.
• Identify ways to mitigate risk and developing new policies/procedures in support of the control and governance agenda.
• Take ownership for managing risk and strengthening controls in relation to the work done.
• Engage in complex analysis of data from multiple sources of information, internal and external sources such as procedures and practises (in other areas, teams, companies, etc).to solve problems creatively and effectively.
• Communicate complex information.
• Influence or convince stakeholders to achieve outcomes.

Requirements & Tech Stack

• Programming & Automation: Python, Bash, SQL.
• 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.

Qualifications

• This role requires a flexible working approach, ensuring availability during select hours that overlap with US-based partners and stakeholders.
• You may be assessed on key essential skills relevant to succeed in role, such as risk and controls, change and transformation, business acumen, strategic thinking and digital and technology, as well as job-specific technical skills.