MLOps Engineer

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Requirements:

• Technical proficiency in Python and ML frameworks such as TensorFlow, PyTorch, or Scikit-Learn.
• Hands-on experience with containerization and orchestration tools like Docker and Kubernetes.
• Proven experience developing and managing CI/CD pipelines using Jenkins, Git, and Terraform.
• Experience with cloud platforms like AWS, Azure, or GCP and infrastructure-as-code practices.
• Proven ability to conduct threat modeling and implement vulnerability management practices.
• Experience working in secure, high-assurance environments, ideally in defense or regulated settings.
• Ability to collaborate across teams and communicate technical specifications effectively.
• Strong troubleshooting skills for diagnosing model and infrastructure-related issues.

Responsibilities:

• Collaborate with cross-functional teams to align MLOps solutions with business objectives.
• Write scripts to automate ML workflows and ensure reproducibility.
• Configure and maintain ML deployment environments using platforms like Kubernetes and Docker.
• Build and maintain CI/CD pipelines for automated model lifecycles.
• Conduct regular performance reviews and audits of deployed models.
• Participate in threat modeling and implement vulnerability management practices.
• Troubleshoot and resolve issues with model performance and infrastructure.
• Ensure adherence to best practices in security, scalability, and compliance.
• Identify and implement reusable solutions to maximize development efficiencies.
• Work with data architects to integrate MLOps pipeline within the overall data architecture.