AI DevOps and Cloud Infrastructure Engineer
Role details
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Tech stack
Job description
The AI DevOps and Cloud Infrastructure Engineer I (Senior Staff) designs, builds, and operates scalable, secure, and highly automated cloud environments that support the training, deployment, monitoring, and continuous delivery of AI and machine learning systems. This role serves as a subject-matter expert in infrastructure automation, distributed compute orchestration, and cloud platform operations, ensuring AI workloads perform reliably across development, staging, and production environments.
The engineer collaborates closely with AI engineering, MLOps, data engineering, platform, and security teams to define infrastructure requirements, improve observability, and support the performance demands of predictive and generative AI workloads. As a senior staff-level contributor, the role establishes best practices, evaluates emerging cloud and AI infrastructure tooling, and mentors' junior engineers to advance DevOps maturity, reliability, and cost efficiency across the organization.
- Architecting and maintaining cloud infrastructure for AI model training, inference services, and distributed compute workloads.
- Implementing infrastructure-as-code (IaC) to automate provisioning, configuration, scaling, and lifecycle management of cloud resources.
- Designing and operating CI/CD pipelines for automated model training, testing, and deployment of AI-enabled applications.
- Optimizing Kubernetes clusters, GPU utilization, and compute scaling strategies to balance performance, reliability, and cost.
- Integrating AI models, inference endpoints, and data pipelines into cloud-native platforms.
- Developing monitoring, logging, alerting, and observability solutions using modern telemetry and tracing tools.
- Troubleshooting issues across networking, containers, compute, storage, and model-serving layers.
- Leading performance benchmarking, load testing, and reliability validation for AI systems.
- Documenting infrastructure architectures, operational runbooks, and engineering standards.
- Supporting automation for dataset ingestion, model versioning, artifact management, and ML testing.
- Ensuring compliance with cloud security, identity management, encryption, and responsible AI guidelines.
- Partnering with security teams to implement secure networking, IAM policies, and secrets management.
- Providing technical mentorship, design reviews, and cloud best-practice guidance to junior engineers.
- Evaluating new cloud services, platform capabilities, and AI infrastructure tooling for adoption.
Requirements
- 4+ years of experience in DevOps, cloud engineering, platform engineering, or infrastructure engineering.
- Strong proficiency with Kubernetes, Docker, and cloud orchestration platforms.
- Deep experience with CI/CD systems and deployment automation.
- Demonstrated ability to debug distributed systems and cloud networking issues.
- Proficiency in Python, Bash, or other automation/scripting languages.
- Strong communication skills and ability to collaborate across engineering and security teams.
- Willingness to travel occasionally for cross-functional planning and collaboration., * Bachelor's degree in Computer Science, Cloud Engineering, Information Systems, or a related technical field, or equivalent experience.
- Master's degree in a technical discipline.
- Experience enabling ML or AI workloads at scale in production environments.
- Cloud and platform certifications, including Azure (AZ-900, AZ-104, AZ-305, AZ-700, AI-102) or equivalent AWS/GCP certifications.
- Advanced experience with AWS (e.g., EKS, EC2, IAM, Lambda, SageMaker) and/or Azure (e.g., AKS, VMSS, Azure ML).
- Experience with GPU orchestration and scaling strategies for AI workloads.
- Expertise with Terraform or other infrastructure-as-code frameworks.
- Hands-on experience with observability stacks such as Prometheus, Grafana, CloudWatch, and OpenTelemetry.
- Experience deploying and operating generative AI workloads, including LLM inference autoscaling and RAG architectures.
- Familiarity with vector database hosting (e.g., Pinecone, Weaviate, FAISS) and model-serving frameworks (e.g., Hugging Face TGI, vLLM, custom inference containers).
- Experience building CI/CD pipelines for LLM fine-tuning workflows (e.g., LoRA, QLoRA, PEFT) and monitoring generative AI performance metrics such as latency, throughput, and hallucination rates.
Benefits & conditions
The wage range for this role takes into account the wide range of factors that are considered in making compensation decisions including but not limited to skill sets; experience and training; licensure and certifications; and other business and organizational needs. The disclosed range estimate has not been adjusted for the applicable geographic differential associated with the location at which the position may be filled. At Crowe, it is not typical for an individual to be hired at or near the top of the range for their role and compensation decisions are dependent on the facts and circumstances of each case. A reasonable estimate of the current range is $74,100.00 - $147,800.00 per year.
Our Benefits: Your exceptional people experience starts here. At Crowe, we know that great peopleare what makes a great firm. We care about our people and offer employees a comprehensive total rewards package. Learn more about what working at Crowe can mean for you!
How You Can Grow: We will nurture your talent in an inclusive culture that values diversity. You will have the chance to meet on a consistent basis with your Career Coach that will guide you in your career goals and aspirations. Learn more about where talent can prosper!