Apply on Employer Site

Crowe · 5 days ago

AI DevOps and Cloud Infrastructure Engineer

Chicago IL USA

Full-time

Onsite

Senior Level, Lead/Staff

$74K/yr - $148K/yr

4+ years exp

Crowe is a leading public accounting, consulting, and technology firm in the United States, known for its commitment to innovation and client service. The AI DevOps and Cloud Infrastructure Engineer will design and maintain scalable cloud environments for AI and machine learning systems, collaborating with various teams to ensure optimal performance and reliability of AI workloads.

AccountingAdviceConsultingFinanceFinancial ServicesInformation TechnologyProfessional ServicesTax Consulting

No H1B

Responsibilities

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

Qualification

KubernetesCI/CD systemsCloud orchestrationInfrastructure automationPythonDockerTerraformAWSAzureCommunicationMentorshipCollaboration

Required

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

Preferred

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