Deliver infrastructure and platforms to enable deployment and monitoring of ML solutions in production

Improve ML solution performance and scalability on AWS

Work closely with data science teams to build AI/ML workflows and environments leveraging AWS services

Coordinate with R&D data scientists to operationalize ML pipelines, models, and algorithms

Own software engineering lifecycle from design through implementation, testing, and maintenance

Drive technology initiatives from initial concept through to project completion

Collaborate with cross-functional teams to integrate advances in Data Processing and AI into the technology stack

Extensive experience working with AWS cloud infrastructure, including 3+ years in DevOps or related roles

Proficient knowledge of AWS services such as SageMaker, Athena, S3, EC2, RDS, Glue, Lambda, Step Functions, EKS, and ECS

Hands-on experience with DevOps tools including Docker and Git

Strong skills in infrastructure as code using Terraform, Ansible, and CloudFormation

Advanced programming ability in Python

Background managing enterprise platforms and responding to client demands and feature requests

Experience with containerization and microservices architectures like Kubernetes, Docker, and serverless systems

Proven experience with CI/CD pipelines including CodePipeline, CodeBuild, and CodeDeploy

Familiarity with GxP compliance standards

Excellent communication, analytical thinking, and problem-solving capabilities

English proficiency at Upper-Intermediate (B2) level

AWS or related cloud technology certifications

Familiarity with large-scale data processing frameworks such as Hadoop or Spark

Experience using data science tools like R and Jupyter notebooks

Exposure to multi-cloud environments including AWS, Azure, or GCP

Skills in mentoring, coaching, and supporting peers and clients

Understanding of SAFe agile methodologies

Practical experience building MLOps environments ready for production