Found Description
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.
- 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 workfl...