Cloud Infrastructure Engineer; AI​/ML

Position: Cloud Infrastructure Engineer (AI/ML)
Job Description Summary Are you passionate about building reliable cloud infrastructure that helps researchers innovate faster? In this role, you will work at the intersection of cloud engineering, machine learning operations, and research computing, helping teams develop, test, and deploy cutting-edge solutions that advance MIM Research initiatives.

You'll collaborate with researchers, engineers, and infrastructure partners to create scalable AWS-based environments, develop internal tools, and build engineering solutions that enable impactful research. We are looking for someone who enjoys solving complex problems, learning new technologies, and working in a collaborative, mission-driven environment.

Job Description

Key Responsibilities In this role, you will:

Partner with Dev Ops 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.

Required Qualifications Education & Experience Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience.
2 to 4 years of experience in Dev Ops, 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, EFSData:
DynamoDBMachine Learning:
Sage Maker (training, pipelines, and deployment)

Experience with Infrastructure as Code (IaC) tools such as Ansible, Terraform, Cloud Formation, or AWS CDK.Familiarity with containerization and orchestration technologies such as Docker, Docker Compose, and Kubernetes.

Dev Ops & Data Engineering Experience building and maintaining continuous integration and continuous deployment (CI/CD) systems, including tools such as Git Hub Actions, Git Lab 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.

Preferred Qualifications 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 RHCECKADAWS Certified Solutions Architect – Associate Or equivalent hands-on experience

What Success Looks Like 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…