Cloud Infrastructure Engineer (AI/ML)

In this role, you will:

• Partner with DevOps and infrastructure teams to migrate, optimize, and support research workloads on AWS cloud platforms.
• Design, build, and maintain machine learning operations (MLOps) pipelines that support model training, evaluation, deployment, and monitoring.
• Develop prototypes and internal tools that accelerate experimentation, model development, and research workflows.
• Translate research objectives into scalable, maintainable, and well-documented engineering solutions.
• Promote and support engineering best practices, including:
  • Code quality, testing, and reliability
  • Documentation and version control
  • Data management and governance
  • Experiment tracking and reproducibility
• Effectively manage multiple projects while balancing fast-paced research needs with long-term engineering sustainability.
• Provide technical guidance and mentorship to early-career engineers and support knowledge sharing across teams.
• Collaborate closely with research scientists, product teams, and infrastructure partners to deliver impactful solutions.

Education & Experience

• Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience.
• 2 to 4 years of experience in DevOps, Site Reliability Engineering (SRE), cloud infrastructure, or related technical roles.
• Experience supporting production systems within Amazon Web Services (AWS).

Cloud & Infrastructure

• Experience working with AWS services such as:
  • Compute: EC2, ECS, EKS, Lambda
  • Storage: S3, EFS
  • Data: DynamoDB
  • Machine Learning: SageMaker (training, pipelines, and deployment)
• Experience with Infrastructure as Code (IaC) tools such as Ansible, Terraform, CloudFormation, or AWS CDK.
• Familiarity with containerization and orchestration technologies such as Docker, Docker Compose, and Kubernetes.

DevOps & Data Engineering

• Experience building and maintaining continuous integration and continuous deployment (CI/CD) systems, including tools such as GitHub Actions, GitLab CI, or Jenkins.
• Strong foundation in Linux systems administration.
• Experience with monitoring and observability practices using tools such as Prometheus, Datadog, or similar technologies.

Programming & Software Engineering

• Understanding of software engineering best practices, including:
  • Software design patterns
  • API development (REST and gRPC)
  • Testing methodologies and maintainable code architecture

Research & Applied Machine Learning

• Experience supporting research environments or collaborating closely with research teams.
• Ability to work effectively with evolving requirements, experimentation, and iterative development processes.

Collaboration & Leadership

• Ability to lead technical initiatives involving multiple stakeholders and cross-functional teams.
• Experience mentoring engineers and supporting the adoption of engineering best practices.
• Strong communication skills with the ability to connect technical concepts across research and engineering audiences.

While not required, the following experiences would be valuable:

• Experience with large-scale distributed computing frameworks such as Spark or Ray.
• Background in high-performance computing (HPC) or research computing environments.
• Familiarity with data governance, compliance requirements, or regulated industries.
• Contributions to open-source projects or published research.
• Relevant certifications such as:
  • RHCSA or RHCE
  • CKAD
  • AWS Certified Solutions Architect – Associate
  • Or equivalent hands-on experience

In this role, you will help create an environment where:

• Research teams can efficiently train, evaluate, and deploy machine learning models.
• Reliable and scalable infrastructure enables research teams to innovate with confidence.
• Best practices for reproducibility, testing, governance, and documentation are consistently adopted.
• Engineers at all levels receive mentorship and opportunities to grow.
• Projects are delivered effectively and aligned with organizational priorities.
• Research and engineering teams work together seamlessly to accelerate meaningful outcomes.

• Help build technology that empowers researchers and drives innovation.
• Work alongside collaborative teams of researchers, engineers, and technical leaders.
• Contribute to meaningful projects with real-world impact.
• Grow your technical expertise across cloud infrastructure, machine learning operations, and research computing.
• Share knowledge, mentor others, and continue developing your leadership skills in a supportive environment.
• Be part of a culture that values diverse perspectives, continuous learning, and inclusive collaboration.

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