Role & Responsibilities Design, develop, and deploy end-to-end machine learning solutions, including large language models (LLMs), embeddings, and multi-agent systems. Build robust data pipelines and manage model lifecycles from experimentation to production deployment. Operationalize AI workflows and integrate autonomous agentic systems into enterprise applications. Containerize applications using Docker, orchestrate deployments via Kubernetes, and maintain CI/CD pipelines for ML/AI projects. Collaborate with data scientists, engineers, and stakeholders to convert research prototypes into production-grade solutions.
Skills Required Strong proficiency in Python, machine learning frameworks, and hands-on experience with MLOps tools such as MLflow and Weights & Biases (W&B). Experience with LLMs, AI agents, Retrieval-Augmented Generation (RAG) systems, and orchestrating multi-agent workflows. Knowledge of DevOps practices, Docker, Kubernetes, GPU/cloud optimization, and CI/CD pipeline...