AI Engineer 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 pipelines. - Solid foundation in statistics, linear algebra, optimization, and feature engineering for machine learning. - Excellent problem-solving, analytical, and collaboration skills, with a continuous learning mindset.