Description

We are seeking a highly experienced Principal MLOps Engineer with 5–10 years of industry experience to lead the design, deployment, and optimization of machine learning infrastructure. This role requires deep expertise in Kubernetes (K8s), cloud-native technologies, and scalable ML systems. The ideal candidate will drive best practices in MLOps and collaborate with cross-functional teams to deliver robust AI solutions. We offer competitive compensation and benefits, opportunity to work on cutting-edge AI/ML systems, collaborative and innovative work environment, career growth and learning opportunities.

Responsibilities

• Architect and maintain scalable MLOps pipelines for model training, deployment, and monitoring in Azure, onprem and other cloud environments
• Lead the implementation of containerized ML workloads using Kubernetes.
• Collaborate with data scientists and engineers to productionize ML models.
• Automate model lifecycle management including versioning, rollback, and performance tracking.
• Ensure high availability, security, and compliance of ML systems.
• Develop infrastructure as code using tools like Terraform or Helm.
• Establish and enforce best practices for model governance and reproducibility.
• Lead other MLOps resources for scaling projects
• Knowledge of Generative AI Projects.

Required Experience and Skills

• Bachelor’s degree in Computer Science, Engineering, or related field (Master’s preferred).
• 10 -12 years of experience in MLOps, DevOps, or software engineering.
• Extensive experience with Kubernetes and container orchestration.
• Proficiency in Python and Bash scripting.
• Experience with ML frameworks such as TensorFlow, PyTorch, or Scikit-learn.
• Familiarity with cloud platforms (AWS, Azure, GCP).
• Knowledge of CI/CD tools and monitoring systems
• Knowledge of using Code assist tools.
• Agentic Frameworks like LangChain – Langraph, Microsoft Azure Foundry
• Knowledge of setting up and maintaining on prem deployments and hybrid environments with cloud

Desired Experience and Skills

Experience with Kubeflow, MLflow, or similar platforms.
Exposure to data versioning tools like DVC or LakeFS.
Understanding of model explainability and compliance frameworks.
Contributions to open-source MLOps projects.