Secondary skills: Sagemaker, Lambda, and Python

Mode of work: Work from Office

Experience: 5-7 Years

About the job:

As a MLops Engineer, you will play a crucial role in developing and implementing robust MLops infrastructures, ensuring data integrity, optimizing ML workflows, and enabling advanced analytics capabilities. You will work closely with cross-functional teams to understand business requirements, design CICD solutions, and collaborate with DS team across the organization.

Know your team:

At ValueMomentum’s Engineering Center, we are a team of passionate engineers who thrive on tackling complex business challenges with innovative solutions while transforming the P&C insurance value chain. We achieve this through strong engineering foundation and continuously refining our processes, methodologies, tools, agile delivery teams, and core engineering archetypes. Our core expertise lies in six key areas: Cloud Engineering, Application Engineering, Data Engineering, Core Engineering, Quality Engineering, and Domain expertise.

Join a team that invests in your growth. Our Infinity Program empowers you to build your career with role-specific skill development leveraging immersive learning platforms. You'll have the opportunity to showcase your talents by contributing to impactful projects.

Responsibilities:

• Model Deployment: Deploy, monitor, and manage machine learning models in AWS environments (SageMaker, EC2, Lambda).
• Automation: Develop and maintain CI/CD pipelines for ML workflows using tools like Gitlab,AWS CodePipeline, CodeBuild, and Jenkins.
• Infrastructure Management: Design and manage scalable, reliable, and cost-effective AWS infrastructure for ML workloads (S3, RDS, DynamoDB, etc.).
• Monitoring and Logging: Implement monitoring and logging solutions to ensure models are performing as expected (CloudWatch, Sagemaker Model Monitor).
• Collaboration: Work closely with Data Scientists and DevOps teams to integrate ML models into production environments.

Requirements:

• 4+ years of experience in MLOps, DevOps, or related fields.
• Should be able to drive the requirements, follow-up, collaboration with cross teams
• Hands-on experience with AWS services like SageMaker, EC2, Lambda, S3, and RDS.
• Proficiency in Python and experience with ML frameworks like TensorFlow, PyTorch, or Scikit-Learn.
• Experience with CI/CD tools and best practices
• Familiarity with Infrastructure as Code (IaC) using tools like Terraform or AWS CloudFormation.
• Knowledge of data engineering tools and practices.
• Knowledge of Kubernetes or Docker.
• Effective communication and collaboration skills, with the ability to effectively interact with stakeholders at all levels.