Machine Learning Operations Engineer - Manager

AI / ML Engineer

Project Role : AI / ML Engineer
Project Role Description : Develops applications and systems that utilize AI tools, Cloud AI services, with proper cloud or on-prem application pipeline with production ready quality. Be able to apply GenAI models as part of the solution. Could also include but not limited to deep learning, neural networks, chatbots, image processing.
Must have skills : DevOps Architecture
Good to have skills : NA
Minimum 12 year(s) of experience is required
Educational Qualification : 15 years full time education

Job Title: Machine Learning Ops Engineer – Manager Level Job Family: Cloud Infrastructure Design Role Description: As a Machine Learning Ops Engineer at the Manager level, you will lead the design and implementation of robust, scalable infrastructure to support machine learning workflows. Your primary responsibility is to enable seamless model training, deployment, and lifecycle management through automation and CI/CD practices. You will collaborate with data scientists, cloud architects, and DevOps teams to ensure efficient and secure ML pipelines across enterprise environments. Key Responsibilities: Infrastructure Design: Architect and optimize cloud-based infrastructure for ML model training and inference at scale. CI/CD for ML: Implement and manage continuous integration and continuous deployment pipelines tailored for ML workflows, ensuring reproducibility and automation. Model Lifecycle Management: Oversee deployment, monitoring, and versioning of ML models in production environments. Scalability & Performance: Design solutions to handle large-scale data processing and distributed training across cloud platforms. Security & Compliance: Ensure ML pipelines adhere to enterprise security standards and regulatory compliance. Collaboration: Work closely with cross-functional teams to integrate ML Ops best practices into product development cycles. Required Skills & Experience: Strong expertise in MLOps frameworks (e.g., MLflow, Kubeflow, TFX). Proficiency in CI/CD tools (e.g., Jenkins, GitHub Actions, Azure DevOps). Hands-on experience with cloud platforms (Azure, AWS, GCP) and containerization (Docker, Kubernetes). Solid understanding of machine learning workflows, data pipelines, and model deployment strategies. Programming skills in Python and familiarity with ML libraries (TensorFlow, PyTorch). Experience in infrastructure as code (Terraform, ARM templates). Excellent leadership and stakeholder management skills. Preferred Qualifications: Experience in distributed training and GPU/TPU optimization. Knowledge of monitoring and observability tools for ML systems. Familiarity with enterprise compliance frameworks for AI/ML solutions.