Senior AWS MLOps Engineer

Company Description

Blend is a premier AI services provider, committed to co-creating meaningful impact for its clients through the power of data science, AI, technology, and people. With a mission to fuel bold visions, Blend tackles significant challenges by seamlessly aligning human expertise with artificial intelligence. The company is dedicated to unlocking value and fostering innovation for its clients by harnessing world-class people and data-driven strategy. We believe that the power of people and AI can have a meaningful impact on your world, creating more fulfilling work and projects for our people and clients. For more information, visit www.blend360.com

Job Description

We are seeking a highly skilled AWS MLOps Engineer with a minimum of 5 years of DevOps experience, particularly in managing ML pipelines in AWS. The ideal candidate has successfully built and deployed at least two MLOps projects using Amazon SageMaker or similar services, with a strong foundation in infrastructure as code and a keen understanding of MLOps best practices.

Key Responsibilities:

• Maintain and enhance existing ML pipelines in AWS with a focus on Infrastructure as Code using CloudFormation.

• Implement minimal but essential pipeline extensions to support ongoing data science workstreams.

• Document infrastructure usage, architecture, and design using tools like Confluence, GitHub Wikis, and system diagrams.

• Act as the internal infrastructure expert, collaborating with data scientists to guide and support model deployments.

• Research and implement optimization strategies for ML workflows and infrastructure.

• Work independently and collaboratively with cross-functional teams to support ML product deployment and re-platforming initiatives.

Qualifications

• 5+ years of hands-on DevOps experience with AWS Cloud.

• Proven experience with at least two MLOps projects deployed using SageMaker or similar AWS services.

• Strong proficiency in AWS services: SageMaker, ECR, S3, Lambda, Step Functions.

• Expertise in Infrastructure as Code using CloudFormation for dev/test/prod environments.

• Solid understanding of MLOps best practices and Data Science principles.

• Proficient in Python for scripting and automation.

• Experience building and managing Docker images.

• Hands-on experience with Git-based version control systems such as AWS CodeCommit or GitHub, including GitHub Actions for CI/CD pipelines.