Virtusa
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Job Summary
We are looking for an experienced AWS SageMaker Engineer / Architect to design, build, and maintain our machine learning infrastructure and pipelines. The ideal candidate will have a strong background in data engineering, with extensive hands-on experience in AWS services, particularly SageMaker. A deep understanding of MLOps principles and experience with Databricks on AWS are highly preferred. This role will be critical in enabling our data scientists to efficiently develop, deploy, and manage machine learning models at scale.
Responsibilities
- Design, implement, and optimize scalable and secure machine learning platforms and MLOps pipelines on AWS, primarily using AWS SageMaker.
- Develop and manage data ingestion, transformation, and storage solutions for machine learning workloads, leveraging various AWS data services (e.g., S3, Glue, Lambda, Athena, Redshift).
- Collaborate with data scientists and machine learning engineers to understand their needs and translate them into robust and efficient engineering solutions.
- Implement and maintain CI/CD pipelines for machine learning models, ensuring automated testing, deployment, and monitoring.
- Containerize machine learning models and applications using Docker for deployment on AWS SageMaker endpoints or other compute services.
- Monitor the performance and health of deployed models and infrastructure, implementing alerting and logging solutions.
- Troubleshoot and resolve complex technical issues related to ML infrastructure and pipelines.
- Stay current with the latest AWS services, SageMaker features, and MLOps best practices, advocating for and implementing new technologies where appropriate.
- If applicable, design and implement data pipelines and machine learning workflows utilizing Databricks on AWS, optimizing for performance and cost.
- Ensure data governance, security, and compliance standards are met across all ML operations.
- Provide technical guidance and mentorship to junior engineers and data scientists.
Required Qualifications
- Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related quantitative field.
- 5-10 years of hands-on experience in Data Engineering, with a strong focus on building and maintaining large-scale data pipelines.
- Proven expertise in AWS services, with significant experience in AWS SageMaker (e.g., SageMaker Studio, Training Jobs, Batch Transform, Endpoint Deployment, Pipelines, Feature Store).
- Solid understanding of cloud architecture principles and best practices.
- Proficiency in Python programming for data manipulation, scripting, and automation.
- Experience with Infrastructure as Code (IaC) tools such as AWS CloudFormation or Terraform.
- Strong understanding of database concepts (relational and NoSQL) and experience with AWS databases (e.g., RDS, DynamoDB).
- Excellent problem-solving skills and the ability to troubleshoot complex technical issues.
- Strong communication and collaboration skills, with the ability to work effectively with cross-functional teams.
Preferred Qualifications
- Hands-on experience with Databricks on AWS, including Spark optimization, Delta Lake, and Databricks notebooks for data engineering and machine learning workflows.
- Demonstrable MLOps experience including continuous integration/continuous delivery (CI/CD) for machine learning models, model monitoring, versioning, and governance.
- Experience with other AWS machine learning services (e.g., Rekognition, Comprehend, Textract) is a plus.
- Familiarity with container orchestration technologies like Kubernetes (EKS) for ML deployments.
- Experience with data visualization tools (e.g., Tableau, Power BI, QuickSight).
- AWS Certifications (e.g., AWS Certified Machine Learning - Specialty, AWS Certified Solutions Architect - Professional).
Seniority level
Director
Employment type
Full-time
Job function
Engineering and Information Technology
Industries
IT Services and IT Consulting
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