Must Have:
Machine Learning Operations (MLOps) Key Responsibilities
Design, build and maintain ML/LLM pipelines for training, fine-tuning, evaluation and deployment.
Operationalize LLM solutions from experimentation to production settings.
Build and monitor observability for LLM applications, including latency, throughput, hallucinations, drift and prompt performance.
Implement RAG workflows, prompt management and versioning.
Automate model validation, testing and CI/CD pipelines.
Ensure security, compliance and data governance for AI/ML solutions.
Design and optimize infrastructure required for MLOps/LLMOps.
Work with cloud-based AI services and multi-cloud environments.
Robust understanding of LLMOps infrastructure and deployment.
Experience with ML/LLM pipelines and productionization.
Knowledge of RAG, LLM evaluation, monitoring and observability.
Experience with machine learning frameworks.
Good exposure to cloud AI services.
Multi-cloud experience.
Good to Have
Experience with LLM serving frameworks and infrastructure.
Experience with model optimization and production deployment.
Knowledge of AI security, governance and responsible AI practices.
Note:
The role focuses on building
production-ready AI/ML and LLM solutions
, with strong emphasis on MLOps, LLMOps, cloud and operational excellence.