Apply on Employer Site

Crowe · 18 hours ago

AI DevOps and Cloud Infrastructure Engineer

Louisville, KY

Full-time

Hybrid

Senior Level

$102K/yr - $204K/yr

7+ years exp

Crowe is a leading public accounting, consulting, and technology firm in the United States, focused on leveraging AI to transform clients' business models. The AI DevOps and Cloud Infrastructure Engineer will lead teams in designing and managing AI/ML infrastructure and cloud platforms, ensuring efficient operations and compliance with security standards.

AccountingAdviceConsultingFinanceFinancial ServicesInformation TechnologyProfessional ServicesTax Consulting

No H1B

Responsibilities

Leading engineering teams responsible for AI/ML infrastructure, cloud operations, and MLOps automation

Defining cloud, Kubernetes, and infrastructure strategy to support scalable model training, inference, and generative AI platforms

Guiding the design and operation of distributed compute environments, GPU clusters, and vector database infrastructure

Overseeing CI/CD pipelines that automate model training, testing, deployment, monitoring, and lifecycle management

Managing incident response, failure analysis, and reliability engineering across AI platforms

Directing performance testing, capacity planning, and cost optimization for AI infrastructure

Ensuring compliance with cloud security, IAM practices, governance requirements, and responsible AI frameworks

Implementing multi-cloud resilience patterns, high availability, and automated failover for critical AI workloads

Supporting platform modernization initiatives, including adoption of optimized LLM runtimes and new orchestration technologies

Evaluating third-party infrastructure tools, GPU scheduling solutions, and platform enhancements

Communicating system status, dependencies, risks, and technical decisions to senior leadership

Managing 4–5 direct reports, including coaching, performance management, and career development

Owning project delivery, including budget, timelines, and quality of outcomes

Coordinating with sales and stakeholders on project sizing, feasibility, and strategic opportunities

Driving continuous improvement initiatives to advance DevOps maturity and AI infrastructure operational readiness

Qualification

DevOpsCloud EngineeringMLOpsKubernetesInfrastructure-as-CodePythonBashMonitoringReliability EngineeringObservabilityCoachingLeadershipCommunicationCollaborationStrategic Decision-Making

Required

7+ years of professional experience in DevOps, cloud engineering, MLOps, or platform engineering

2+ years of experience in engineering leadership or senior technical leadership roles

Expert proficiency with distributed cloud systems, Kubernetes, and infrastructure-as-code

Advanced ability to troubleshoot infrastructure, networking, container, and deployment issues

Proficiency in Python, Bash, or similar automation and scripting languages

Strong understanding of monitoring, observability, and reliability engineering patterns

Hands-on experience supporting infrastructure for ML or generative AI workloads

Strong leadership, communication, and cross-functional collaboration skills

Preferred

Bachelor's degree in computer science, engineering, cloud computing, or a related field

Master's degree in technical discipline

Cloud and AI certifications, including Azure (AZ-900, AZ-104, AZ-305, AZ-700, AZ-800, AI-102) or equivalent AWS/GCP certifications

Extensive experience with Kubernetes platforms (EKS, AKS, GKE) and cloud ML services (Azure ML, SageMaker)

Experience with GPU workload orchestration, optimization, and multi-tenant inference environments

Expertise in observability and distributed tracing (Prometheus, Grafana, CloudWatch, OpenTelemetry)

Strong experience with Terraform and infrastructure governance at scale

Familiarity with service mesh architectures (Istio, Linkerd) and advanced deployment patterns (blue/green, canary)

Advanced experience supporting generative AI platforms, including LLM inference runtimes (vLLM, TGI), RAG infrastructure, and vector databases (Pinecone, Weaviate, FAISS)

Experience operating fine-tuned LLMs (LoRA, QLoRA), managing GenAI CI/CD pipelines, and implementing hallucination, drift, and reliability monitoring

Demonstrated ability to make strategic technical decisions within defined delivery and budget constraints