Senior Software Engineer leading software development for Marathon Petroleum. Focusing on Azure technologies and driving innovation in a collaborative team environment.
About the role
- Cloud Infrastructure Analyst internship involved in AI Ops and cloud platform initiatives at Sun Life. Contributing to infrastructure, automation, and operational workflows with a focus on continuous improvement.
Responsibilities
- Assist with enabling and supporting cloud based AI services and solutions (e.g., AWS based platforms and tooling)
- Support infrastructure provisioning using Infrastructure as Code (CloudFormation and/or Terraform)
- Help implement and improve automation, CI/CD pipelines, and operational workflows
- Assist with onboarding and supporting teams using shared AI and analytics platforms
- Contribute to documentation, runbooks, and knowledge base articles
- Participate in Agile ceremonies and collaborate with engineers, architects, and platform partners
- Learn how enterprise AI and analytics platforms are designed, deployed, and supported in production environments
- Observe and assist with operational support activities (monitoring, incident follow‑ups, KTLO tasks)
- Identify opportunities for process improvements and suggest practical, innovative solutions
Requirements
- Strong communication and collaboration skills
- Insatiable curiosity and eagerness to learn and apply new technologies
- Strong analytical and problem-solving abilities; comfortable breaking down problems and asking questions
- Ability to take initiative and follow through on assigned tasks
- Ability to work both independently and as part of a team
- Exposure to cloud platforms (AWS preferred)
- Basic understanding of services such as IAM, S3, or managed AI services
- Familiarity with Infrastructure as Code concepts (specifically CloudFormation)
- Programming or scripting experience, particularly with automation (Python, Bash, PowerShell)
- Basic understanding of CI/CD, Git, or DevOps concepts
- Interest in AI, machine learning, analytics platforms, or platform engineering
Benefits
- Hands-on experience with real enterprise AI and cloud platforms
- Mentorship from experienced cloud, AI, and platform engineers
- Exposure to modern DevOps, automation, and AI Ops practices
- Opportunities to contribute meaningful work that supports teams across the organization
- A hybrid work environment aligned to team and business needs
- Access to learning resources, training, and career development pathways
Job title
Job type
Experience level
Salary
Not specified
Degree requirement
Tech skills
Location requirements
Senior Cloud Engineer implementing enterprise cloud solutions for Core Specialty's cloud engineering team. Focusing on Microsoft Azure and Infrastructure as Code in a hybrid work setting.
AWS Cloud Engineer joining a cloud transformation project for UK Biobank. Developing and maintaining AWS platforms for world - leading biomedical research.
Service Engineer focusing on cloud infrastructure and network operations in a diverse and inclusive environment. Committed to innovation and operational excellence within NTT DATA.
Senior IT Cloud Engineer deploying and maintaining cloud services in Azure for financial solutions company. Responsibilities include implementing security best practices and ensuring system reliability.
Senior Cloud Architect managing AWS services and infrastructure for fintech projects. Focus on Vault, Terraform, and Kubernetes with a diverse global team.
Cloud Engineer at M&G designing and delivering Azure infrastructure solutions. Collaborating on projects and mentoring others in best practices within cloud deployment.
Cloud Engineer implementing scalable Azure solutions for product teams at BearingPoint. Utilizing cutting - edge technologies and collaborating with clients for innovative solutions.
Cloud Engineer implementing Azure solutions for product development at BearingPoint. Collaborating with teams on operational enhancements and cloud maturity initiatives in a hybrid work environment.
AWS Cloud Engineer optimizing AWS environments for data science workloads. Collaborating with data and ML teams on cloud architecture and ML infrastructure.