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....