ML-Ops Engineer

E-IT; Charlotte (NC); On-site; USD 120,000 - 180,000; Full time

Job summary

E-IT in Charlotte, NC, seeks a senior cloud/platform engineer to design and operate cloud-native platforms across AWS and Azure, with a focus on GenAI enablement. You will lead multi-account architectures, secure, scalable deployments, and cost-optimized solutions while collaborating with security and product teams.

Expertise in Kubernetes, serverless components, and DevOps tooling is essential, as well as strong software development skills in Python/Java and experience with modern data stores

Qualifications

• Hands-on experience building and operating cloud-native apps on AWS and Azure.
• Expertise in multi-account design, landing zones, security, high availability, disaster recovery, scalability, and cost optimization.
• Experience enabling and operationalizing Enterprise GenAI platforms, including model onboarding, AI gateways, inference platforms, vector databases, RAG, guardrails, and AI enablement.
• Strong knowledge of Azure AI Foundry, Azure OpenAI, AWS Bedrock, SageMaker, model serving, embeddings, prompt engineering.
• Expert-level experience with Docker, Kubernetes/OpenShift, AKS, EKS, service mesh, ingress controllers, autoscaling, and multi-cluster platform operations.
• Strong DevOps and Platform Engineering experience, including GitHub Actions, Azure DevOps, Jenkins, GitOps, ArgoCD, Terraform, Ansible, automated testing, and release management.
• Proficiency in Python, Java, REST APIs, microservices, and distributed systems development.
• Experience with MongoDB, Redis, PostgreSQL, Vector Databases, caching strategies, state management, and high-throughput data platforms.
• Strong understanding of cloud security, IAM, secrets management, observability, monitoring, logging, SRE practices, and production support.
• Experience troubleshooting and optimizing cloud-hosted, containerized workloads for performance, resiliency, scalability, and cost efficiency.
• Ability to partner with application, platform, infrastructure, and security teams to accelerate GenAI and cloud modernization initiatives.

Skills

Cloud-native apps

AWS & Azure architecture

GenAI platforms

AI platforms knowledge (Azure OpenAI,드

Docker & Kubernetes

DevOps/Platform engineering

Python & Java

Databases (MongoDB, Redis, PostgreSQL)

Cloud security & IAM

Observability & SRE

Troubleshooting cloud workloads

Cross-team collaboration

Tools

Docker

Kubernetes/OpenShift

AKS

EKS

ArgoCD

Terraform

Ansible

Jenkins

GitHub Actions

Azure DevOps

Job description

• Strong hands-on experience building and operating cloud-native applications and platforms on AWS and Azure, including VPC/VNet, IAM, Load Balancers, API Gateway, Lambda/Functions, EKS/AKS, ECS, App Services, Storage, Key Vault/Secrets Manager, and cloud networking.
• Deep expertise in AWS and Azure architecture, including multi-account/subscription design, landing zones, cloud security, high availability, disaster recovery, scalability, and cost optimization.
• Experience enabling and operationalizing Enterprise GenAI platforms, including model onboarding, AI gateways, inference platforms, vector databases, RAG, guardrails, and AI application enablement.
• Strong knowledge of Azure AI Foundry, Azure OpenAI, AWS Bedrock, SageMaker, model serving, embeddings, prompt engineering, AI evaluations, and agentic frameworks.
• Expert-level experience with Docker, Kubernetes/OpenShift, AKS, EKS, service mesh, ingress controllers, autoscaling, and multi-cluster platform operations.
• Strong DevOps and Platform Engineering experience, including GitHub Actions, Azure DevOps, Jenkins, GitOps, ArgoCD, Terraform, Ansible, automated testing, and release management.
• Proficiency in Python, Java, REST APIs, microservices, and distributed systems development.
• Experience with MongoDB, Redis, PostgreSQL, Vector Databases, caching strategies, state management, and high-throughput data platforms.
• Strong understanding of cloud security, IAM, secrets management, observability, monitoring, logging, SRE practices, and production support.
• Experience troubleshooting and optimizing cloud-hosted, containerized workloads for performance, resiliency, scalability, and cost efficiency.
• Ability to partner with application, platform, infrastructure, and security teams to accelerate GenAI and cloud modernization initiatives.