**This position requires either US Citizenship or Green Card status. Please do not apply if you do not meet this requirement.**

About the Role:

Be at the forefront of machine learning operations, enabling seamless deployment and management of ML models for leading tech firms. As an MLOps Engineer, you'll streamline AI pipelines, ensuring scalability, reliability, and performance for mission-critical applications in dynamic industries.
       

        Responsibilities:

• Build and maintain CI/CD pipelines for ML model deployment using tools like Kubeflow or MLflow.

• Optimize cloud infrastructure (AWS, Azure, GCP) for high-performance ML workloads.

• Monitor and improve model performance in production, addressing drift and latency issues.

• Collaborate with AI and data teams to automate workflows and enhance efficiency.

• Implement version control for models and datasets to ensure traceability.

• Conduct performance tuning for distributed ML systems to meet enterprise demands.
  

  Qualifications:

• 3+ years in DevOps or MLOps, with a focus on ML pipelines or infrastructure.

• Expertise in Kubernetes, Docker, and cloud platforms (AWS, Azure, GCP).

• Proficiency in Python and scripting for automation of ML workflows.

• Experience with ML frameworks like TensorFlow or PyTorch is a plus.

• Knowledge of monitoring tools (e.g., Prometheus, Grafana) for production systems.

• Strong analytical skills to troubleshoot complex pipeline issues.