Responsibilities:

• Design and implement data pipelines and engineering infrastructure to support enterprise machine learning systems at scale.
• Work closely with data scientists and engineering teams to deploy, monitor, and optimize machine learning models in production.
• Identify, evaluate, and integrate new technologies to enhance performance, maintainability, and reliability of machine learning solutions.
• Apply software engineering best practices to machine learning pipelines, including CI/CD, automation, monitoring, and version control.
• Manage cloud infrastructure (AWS, Azure, GCP) and containerization (Docker, Kubernetes) to ensure scalable and efficient ML workloads.
• Implement and maintain highly available and scalable machine learning environments.
• Ensure the security and compliance of machine learning systems, adhering to governance and industry regulations.
• Troubleshoot and optimize machine learning models and infrastructure for performance improvements.
• Collaborate with IT and OT teams to ensure seamless integration of machine learning systems.
• Use Infrastructure as Code (Terraform, CloudFormation) to automate the management and provisioning of infrastructure.
• Implement automated processes for deployment, monitoring, logging, and performance tracking.

Skills

Required Skillsets:

• ML Model Deployment & Containerization: Strong experience with Docker and Kubernetes.
• Cloud Platforms: Expertise in AWS, Azure, or Google Cloud Platform (GCP).
• DevOps Practices: In-depth knowledge of DevOps, CI/CD pipelines, and automation techniques.
• Monitoring & Logging: Proficiency in setting up monitoring and logging for ML models and infrastructure.
• Version Control: Expertise in Git or other version control systems.
• IT-OT Integration: Experience integrating IT and OT systems.
• Scalability & High Availability: Proven track record of designing scalable, highly available machine learning infrastructure.
• Security & Compliance: Understanding of security protocols, compliance frameworks, and governance.
• Infrastructure as Code (IaC): Proficiency with Terraform or CloudFormation for automating infrastructure management.
• Scripting: Strong skills in Python or Bash scripting for automation.
• Data Engineering: Familiarity with data engineering workflows and handling large datasets.
• Troubleshooting: Excellent problem-solving and troubleshooting abilities in distributed systems.