ML-Ops​/Platform Engineer

Racedog Technologies is actively seeking an elite, highly experienced ML-Ops / Platform Engineer with 12+ years of cloud infrastructure and machine learning operations experience to join our client team in Charlotte, NC on an onsite contract basis. In this critical platform engineering role, you will be responsible for building, scaling, and maintaining robust MLOps pipelines, automated CI/CD workflows, and secure cloud networking architectures.

You will leverage your deep practical expertise in VPC/VNet design, IAM security, Load Balancers, API Gateways, Kubernetes, and machine learning deployment tooling to ensure uncompromised platform scalability. This opportunity requires a senior platform engineer with exceptional cloud networking depth and enterprise deployment experience in Charlotte, NC.

As an ML-Ops / Platform Engineer supporting client operations in Charlotte, NC via Racedog Technologies, you will take full technical ownership of machine learning infrastructure, cloud networking, and automated deployment pipelines. Your day-to-day responsibilities include designing secure Virtual Private Clouds (VPCs/VNets), configuring IAM role-based access controls, managing enterprise load balancers, and setting up API gateways for high-throughput model inference. You will collaborate closely with data science teams, software engineers, and security officers to containerize machine learning models, implement model monitoring, and streamline automated training and inference pipelines.

Furthermore, you will ensure strict adherence to enterprise security compliance and disaster recovery protocols.

• Architect, build, and maintain robust MLOps platforms, automated deployment pipelines, and scalable cloud infrastructure.
• Design and secure cloud networking topologies including VPCs, VNets, subnets, route tables, and security groups.
• Configure and manage enterprise IAM policies, role-based access controls (RBAC), and encryption standards.
• Deploy and administer Application Load Balancers (ALBs), Network Load Balancers (NLBs), and API Gateways for model inference.
• Operationalize machine learning workflows, model training pipelines, and automated model serving architectures on Kubernetes.
• Collaborate with data science and software engineering teams to streamline model containerization and MLOps automation.
• Implement comprehensive infrastructure monitoring, logging, and alerting systems via Prometheus, Grafana, and Cloud Watch.
• Ensure strict adherence to enterprise security compliance, vulnerability management, and disaster recovery standards.

• Bachelor's degree in Computer Science, Information Technology, Software Engineering, or a related technical discipline.
• Minimum of 12+ years of professional IT experience, with extensive specialization in cloud infrastructure and platform engineering.
• Proven hands-on experience designing and managing MLOps platforms and machine learning deployment pipelines.
• Expert practical proficiency in cloud networking (VPCs/VNets, subnets, peering, VPNs) and security (IAM, security groups).
• Extensive experience configuring and managing enterprise Load Balancers and API Gateways.
• Strong proficiency in container orchestration platforms (Kubernetes, Docker) and Infrastructure-as-Code (Terraform/Cloud Formation).
• Exceptional analytical, problem-solving, architectural design, and cross-functional communication capabilities.
• Mandatory eligibility to work in the United States and availability for onsite work in Charlotte, NC.

• Advanced postgraduate degrees in Computer Science or Cloud Computing.
• Professional cloud certifications (AWS Certified Dev Ops Engineer, AWS Certified Solutions Architect, or Azure Dev Ops Engineer).
• Prior professional experience within banking, financial services, or enterprise SaaS sectors in Charlotte, NC.
• Strong background in CI/CD pipeline automation (Jenkins, Git Lab CI, Git Hub Actions).
• Experience with ML model monitoring tools (MLflow, Kubeflow, Arize, or Fiddler).

• Salary/Rate:
  Market Competitive / Standard Consulting Rate
• Deadline:
  Open until filled
• Notice Period:
  Immediate to 30 Days
• Contract Duration:
  Long-Term Contract / On-Site (Charlotte, NC)

When applying for this ML-Ops / Platform Engineer position via Racedog Technologies, ensure your resume explicitly highlights your VPC/VNet cloud networking expertise, API Gateway configuration, and MLOps…