ML-Ops / AI Platform Engineer – Charlotte, NC

Job Summary

We are looking for

strong, hands-on ML-Ops / AI Platform Engineering professionals

for opportunities in

Charlotte, NC

. We anticipate multiple openings, so we are looking to connect with qualified candidates.

Experience:

10+ years

Location:

Charlotte, NC

Role:

ML-Ops / AI Platform Engineer

Key Technical Requirements

•

AWS & Azure:

Strong hands-on experience building and operating cloud-native applications and platforms across AWS and Azure, including VPC/VNet, IAM, Load Balancers, API Gateway, Lambda/Azure Functions, EKS/AKS, ECS, App Services, cloud storage, Key Vault/Secrets Manager, and cloud networking.

•

Cloud Architecture:

Deep expertise in AWS and Azure architecture, including multi-account/subscription strategies, landing zones, cloud security, high availability, disaster recovery, scalability, and cost optimization.

•

Enterprise GenAI Platforms:

Experience enabling and operationalizing enterprise GenAI platforms, including model onboarding, AI gateways, inference platforms, vector databases, RAG, guardrails, and AI application enablement.

•

AI/ML Platforms:

Strong knowledge of

Azure AI Foundry, Azure OpenAI, AWS Bedrock, Amazon SageMaker, model serving, embeddings, prompt engineering, AI evaluation frameworks, and agentic AI frameworks

.

•

Kubernetes & Containers:

Expert-level experience with Docker, Kubernetes/OpenShift, AKS, EKS, service mesh, ingress controllers, autoscaling, and multi-cluster platform operations.

•

DevOps & Platform Engineering:

Strong experience with GitHub Actions, Azure DevOps, Jenkins, GitOps, Argo CD, Terraform, Ansible, automated testing, CI/CD, and release management.

•

Software Engineering:

Proficiency in

Python, Java, REST APIs, microservices, and distributed systems

.

•

Data & Caching Technologies:

Experience with

MongoDB, Redis, PostgreSQL, vector databases, caching strategies, state management, and high-throughput data platforms

.

•

Security & SRE:

Strong understanding of cloud security, IAM, secrets management, observability, monitoring, logging, SRE practices, reliability engineering, and production support.

•

Performance & Reliability:

Proven experience troubleshooting and optimizing cloud-hosted and containerized workloads for

performance, resiliency, scalability, availability, and cost efficiency

.

•

Cross-Functional Collaboration:

Ability to partner effectively with application, platform, infrastructure, architecture, and security teams to accelerate

GenAI adoption and cloud modernization initiatives

.

Ideal Candidate

We are looking for

10+ years of experience as an engineer/architect who is genuinely hands-on

, particularly with cloud platforms, Kubernetes, DevOps/Platform Engineering, and enterprise GenAI technologies.