H1b Candidates Only || MLOPS Engineer at Sunnyvale, CA || Onsite

[Anil Kumar Pal]

Hi

Please find the Job Description below and let me know your interest.

Role: MLOPS Engineer
Location: Sunnyvale, CA || Onsite
Position Type: Contract
Years of experience: 8-10 Years

Role and Responsibilities:

• 6-8+ years experience required in the following skills:
• Digital : Microsoft Azure
• Digital : Machine Learning
• Digital : DevOps
• Digital : Docker

Job Summary:
We are seeking a skilled and proactive Mops Engineer to join our team and help operationalize machine learning models at scale. The ideal candidate will have experience in deploying, monitoring, and maintaining ML models in production environments, and will work closely with data scientists, software engineers, and DevOps teams to ensure seamless integration and performance.

Key Responsibilities:

• Design, build, and maintain scalable ML infrastructure and pipelines.
• Automate model deployment, versioning, and rollback processes.
• Monitor model performance and data drift in production.
• Collaborate with data scientists to transition models from experimentation to production.
• Implement CI/CD workflows for ML systems.
• Ensure compliance with data governance and security standards.
• Optimize resource usage and cost-efficiency of ML workloads.
• Maintain documentation and best practices for Mops processes.

Required Qualifications:

• Bachelor’s or Master’s degree in Computer Science, Engineering, or related field.
• 3+ years of experience in Mops, DevOps, or ML Engineering.
• Proficiency in Python and ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn).
• Experience with cloud platforms (AWS, Azure, GCP) and containerization (Docker, Kubernetes).
• Familiarity with ML lifecycle tools (MLflow, Kubeflow, SageMaker, etc.).
• Strong understanding of CI/CD, Git, and automation tools.
• Excellent problem-solving and communication skills.

Preferred Qualifications:
Experience with data versioning tools (e.g., DVC).
Knowledge of feature stores and model registries.
Exposure to real-time inference and streaming data systems.

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Thanks,

Anil Kumar Pal