AI DevOps Engineer-Hybrid

Title: AI DevOps Engineer

Location: Maryland, USA, Onsite (Hybrid)

Duration: 6-12 Months Contract

We are seeking an experienced AI DevOps Engineer to design, automate, deploy, and maintain scalable infrastructure and CI/CD platforms supporting AI/ML applications. The ideal candidate will have strong expertise in traditional DevOps, cloud infrastructure, Kubernetes, Infrastructure as Code, and CI/CD, combined with hands-on experience deploying and operationalizing machine learning and Generative AI workloads.

Key Responsibilities

• Design and implement CI/CD pipelines for software, machine learning, and AI/GenAI applications.
• Build and manage scalable cloud infrastructure supporting AI/ML workloads.
• Develop automated deployment and release processes using Azure DevOps, GitHub Actions, GitLab CI/CD, Jenkins, or similar technologies.
• Deploy and manage containerized AI/ML workloads using Docker and Kubernetes.
• Implement MLOps/LLMOps practices for model training, deployment, monitoring, versioning, and lifecycle management.
• Automate infrastructure provisioning and configuration using Terraform, Ansible, CloudFormation, or equivalent tools.
• Manage cloud platforms such as AWS, Microsoft Azure, or Google Cloud Platform (Google Cloud Platform).
• Build infrastructure capable of supporting GPU-enabled AI/ML workloads where required.
• Implement monitoring, logging, alerting, and observability for AI/ML applications and infrastructure.
• Support model-serving infrastructure and APIs for machine learning and Generative AI applications.
• Integrate ML models and AI services into enterprise CI/CD and deployment workflows.
• Implement security, identity, access management, secrets management, and compliance controls.
• Troubleshoot infrastructure, deployment, application, and model-serving issues.
• Improve system reliability, scalability, performance, and cost efficiency.
• Collaborate with development, data science, security, and infrastructure teams to establish DevSecOps and MLOps best practices.
• Develop automation scripts and tools using Python, Bash, PowerShell, or similar languages.
• Create and maintain technical documentation, architecture diagrams, deployment procedures, and operational runbooks.

Required Skills:

• Bachelor s degree in computer science, Engineering, Information Technology, or related field.
• 7+ years of experience in DevOps, Cloud Engineering, SRE, Platform Engineering, or related disciplines.
• 3+ years of experience supporting AI/ML, MLOps, or GenAI infrastructure.
• Experience building enterprise-grade AI platforms.
• Experience with GPU infrastructure such as NVIDIA CUDA, GPU Kubernetes workloads, or NVIDIA Triton Inference Server is a strong plus.
• Experience with microservices and API-based architectures.
• Strong troubleshooting and problem-solving skills.
• Excellent communication and collaboration skills.