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Metro location, mid-level experience range, and strong global brand increase applicant density.
Role requires specialized MLOps/LLMOps expertise, making cross-industry transferability limited.
Explicit 5+ years requirement plus extensive mandatory MLOps, cloud, and LLMOps skills.
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Lead design and implementation of end-to-end MLOps and LLMOps pipelines for large language models, covering development, deployment, and continuous monitoring.
Develop automation and CI/CD pipelines to ensure scalable, efficient, and cost-effective model deployment in cloud environments.
Collaborate with cross-functional teams to integrate ML and LLM models into production-ready systems and optimize model performance including hardware acceleration.
Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or related field.
5+ years experience in MLOps with at least 2 years specifically on LLMOps pipelines for large language models.
Proven experience deploying, managing, and optimizing ML models at scale on cloud platforms (AWS, GCP, Azure).
Expertise in containerization (Docker, Kubernetes), CI/CD tools (Jenkins, GitLab CI, CircleCI), and ML frameworks (TensorFlow, PyTorch, Hugging Face).
Experienced in operating large-scale, production-grade AI/ML systems with focus on automation and performance optimization.
Technical leader comfortable managing end-to-end ML deployment lifecycles and collaborating with data scientists and engineers.
Strong cloud infrastructure skills combined with deep knowledge of MLOps, LLMOps workflows, and governance including security and compliance.