Cloud Engineer
Confidential · United States
- Hybrid
- Full-time
- $250,000 / year
- United States
Tailored resume — keyword-matched to this role.
Hiring manager — we find who's hiring.
Intro email — drafted to reach them directly.
Job highlights
- Design, build, deploy scalable AI/ML infrastructure.
- Automate ML pipelines and cloud services.
- Manage containerized services and GPU clusters.
- Ensure observability, governance, and cost control.
- Collaborate with data science and engineering teams.
About the role
About Us
We are a staffing services technology company that helps organizations design, build, and scale digital products and engineering capabilities. Our teams deliver end-to-end software development, engineering, and design services, and we provide flexible staffing solutions to augment internal teams with specialized talent—quickly and reliably.
The Role
We are seeking an innovative and resilient Cloud Engineer to join our distributed engineering team. This role focuses on designing, building, deploying, and operating scalable AI/ML infrastructure that enables product teams to prototype, train, and serve models with reliability and efficiency. You’ll bridge data science, backend engineering, and platform operations to ensure robust, observable, and cost-effective AI systems in production.
What You’ll Do
- Cloud Architecture & Infra Design: Design and implement scalable, secure cloud architectures for AI/ML workloads across multiple environments (dev, staging, prod). Architect data pipelines, model training fleets, model serving endpoints, and incident response playbooks.
- Platform & Automation: Build reusable platform components (CI/CD for ML, feature stores, model registry, experiment tracking, reusable pipelines) and automate deployment, scaling, and self-healing of AI services.
- Model Deployment & Operations: Provision GPU/CPU clusters, manage containerized services (Docker/Kubernetes), implement inference caching, autoscaling, and canary/blue-green deployment strategies; monitor service health and model performance in production.
- Observability & Governance: Instrument comprehensive monitoring, tracing, logging, and alerting; establish SLAs/SLOs for latency, availability, and model quality; implement cost controls and usage dashboards.
- Collaboration & Delivery: Work closely with Data Scientists, ML Engineers, Backend Engineers, and DevOps in an Agile environment to operationalize experiments, standardize APIs, and maintain clear documentation.
What We’re Looking For
- Experience: 3+ years in cloud engineering, DevOps, or MLOps with production-grade systems; experience supporting AI/ML workloads is a plus.
- Education: Bachelor’s or Master’s degree in Computer Science, Electrical/Computer Engineering, Mathematics, or a related field (or equivalent practical experience).
- Cloud & Infra Proficiency: Strong hands-on experience with at least one major cloud provider (AWS, Azure, or GCP); familiarity with Kubernetes, containerization, and cloud-native services for compute, storage, and networking.
- ML Infrastructure: Experience with ML lifecycle tooling (MLflow, Kubeflow, Weights & Biases, or equivalent) and feature stores/ML metadata management concepts; comfort with model serving frameworks and GPUs.
- Automation & CI/CD: Proficient in CI/CD for data/ML workloads, IaC (Terraform, CloudFormation, ARM templates), Git workflows, and configuration management.
- Programming & SRE Practices: Proficiency in Python or another language commonly used in ML ops; strong understanding of software engineering best practices (testing, code reviews, documentation).
Compensation & Benefits
We believe in paying top-of-market rates for top-tier talent. The base salary range for this role is $175,000 to $250,000, with exact placement determined by your skills, years of experience, and interview performance.
- Equity: Competitive stock option package.
- Remote Setup: Home office stipend to get your workspace set up perfectly.
- Health: Comprehensive medical, dental, and vision insurance.
- Time Off: Flexible PTO policy + Company Holidays.
- Growth: Annual learning and development budget.
- Retirement: 401(k) matching plan.
Key skills/competency
- Cloud Engineer
- AI/ML Infrastructure
- DevOps
- MLOps
- Kubernetes
- CI/CD
- Python
- AWS
- GCP
- Azure
Skills & topics
- Cloud Engineer
- DevOps
- MLOps
- AI
- Machine Learning
- Kubernetes
- CI/CD
- AWS
- GCP
- Azure
- Python
- Infrastructure
- Remote
How to get hired
- Tailor your resume: Highlight your 3+ years of cloud engineering, DevOps, or MLOps experience, specifically mentioning AI/ML workloads. Emphasize proficiency in cloud providers (AWS, Azure, GCP), Kubernetes, and CI/CD.
- Showcase automation skills: Detail your experience with IaC tools like Terraform and scripting languages like Python, demonstrating your ability to automate deployments and manage infrastructure.
- Quantify achievements: Use numbers to demonstrate the impact of your work, such as improvements in system scalability, efficiency, or cost savings in previous AI/ML projects.
- Prepare for technical questions: Be ready to discuss cloud architecture design, containerization strategies, ML lifecycle tooling, and SRE practices.
Technical preparation
Master cloud provider services (AWS, Azure, GCP).,Deep dive into Kubernetes and containerization.,Practice IaC with Terraform or CloudFormation.,Build CI/CD pipelines for ML workloads.
Behavioral questions
Describe a complex cloud architecture you designed.,How do you ensure reliability in production systems?,Tell me about a time you automated a process.,How do you collaborate with data scientists?
Frequently asked questions
- What specific AI/ML workloads are prioritized for this Cloud Engineer role at Confidential?
- This Cloud Engineer role at Confidential focuses on building and operating infrastructure for AI/ML workloads, including data pipelines, model training fleets, and model serving endpoints. Experience with ML lifecycle tooling like MLflow or Kubeflow is highly valued to support these efforts.
- What level of experience is expected for a Cloud Engineer applying to Confidential?
- Confidential is seeking candidates with at least 3 years of experience in cloud engineering, DevOps, or MLOps, particularly those with production-grade systems and prior experience supporting AI/ML workloads. A relevant Bachelor's or Master's degree is preferred, but equivalent practical experience is also considered.
- Does Confidential offer opportunities for professional growth for Cloud Engineers?
- Yes, Confidential provides an annual learning and development budget for its employees, including Cloud Engineers. This supports continuous learning and skill enhancement in areas like cloud technologies, AI/ML, and DevOps practices.
- How does Confidential support remote employees for the Cloud Engineer position?
- Confidential offers a home office stipend to help Cloud Engineer hires set up their workspace perfectly for remote work. The company embraces a distributed engineering team model, ensuring remote employees have the resources they need.
- What are the primary cloud platforms used by Confidential for AI/ML infrastructure?
- Confidential has strong hands-on experience requirements for at least one major cloud provider, including AWS, Azure, or GCP. Proficiency in these platforms is crucial for designing and implementing scalable cloud architectures for AI/ML workloads.
- Can an experienced individual contributor without a degree apply for the Cloud Engineer role at Confidential?
- Absolutely. While Confidential prefers a Bachelor's or Master's degree in a related field, they also state 'or equivalent practical experience.' This means significant hands-on experience in cloud engineering, DevOps, or MLOps can certainly qualify you for the Cloud Engineer position.