About this role

ML-Ops / Platform Engineer

Charlotte, NC

12 months

Required Skills

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.

ML-Ops / Platform Engineer

Key skills

About this role