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Strong employer brand, metro location, and mid-level MLOps specialization drive moderate competition.
Specialized MLOps and LLMOps expertise makes background fit highly sensitive across industries.
Explicit 5+ years, 2+ years LLMOps, and extensive mandatory tech stack imply high shortlisting strictness.
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Lead design and implementation of end-to-end MLOps and LLMOps pipelines for model development, deployment, and continuous monitoring in production.
Develop automation and CI/CD pipelines for scalable, efficient deployment and management of large language models in cloud environments.
Build and maintain robust data pipelines and monitoring systems to optimize model performance, cost, speed, and reliability.
Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or related field.
5+ years of experience in MLOps with at least 2 years specifically on LLMOps pipelines for large language models.
Strong expertise in cloud platforms (AWS, GCP, Azure), containerization (Docker, Kubernetes), and CI/CD tools (Jenkins, GitLab CI, CircleCI).
Work Experience Required: 5+ years in MLOps including 2+ years in LLMOps pipelines.
Experienced in deploying and managing large language models (e.g., GPT, BERT) at scale using frameworks like TensorFlow, PyTorch, and Hugging Face.
Skilled in building scalable, production-grade AI systems with a focus on automation, monitoring, and performance optimization using GPUs/TPUs.
Comfortable collaborating with cross-functional teams including data scientists and ML engineers to integrate models into production environments.