ML Platform Engineer

Enexus Global Inc.; Charlotte (NC); On-site; USD 140,000 - 190,000; Full time

Job summary

Enexus Global Inc. is seeking an ML-Ops / Platform Engineer to design, build, and operate cloud-native platforms across AWS and Azure.

You will own multi-account architectures, security, and scalable infrastructure to support GenAI workloads and production-grade services. You will collaborate with application, platform, and security teams to accelerate cloud modernization, implement CI/CD pipelines, and ensure reliable, cost-efficient operations at scale in a strict onsite environment in

Qualifications

• 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.
• 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.
• 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 platforms (AWS/Azure)

GenAI platform enablement

Distributed systems

REST APIs & microservices

Python/Java development

SRE practices & production support

Tools

Docker

Kubernetes/OpenShift

AKS

EKS

Service mesh

Ingress controllers

Autoscaling

Terraform

Ansible

GitHub Actions

Azure DevOps

Jenkins

GitOps

ArgoCD

Job description

Greetings!

ML-Ops / Platform Engineer

Charlote, NC (Onsite)

• 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.