Description

You will architect and build a scalable machine learning platform for training, deployment, and lifecycle management of ML, LLM, and Generative AI models.

Responsibilities

• Architect and build a scalable machine learning platform for training, deployment, and lifecycle management of ML, LLM, and Generative AI models.
• Lead infrastructure development supporting production hosting of complex AI systems, including large-scale inference workloads.
• Design developer-friendly abstractions and automation to simplify model development and deployment processes.
• Implement and enhance MLOps capabilities like experiment tracking, model versioning, CI/CD for ML, monitoring, and reproducibility using Databricks and MLflow.
• Serve as the technical leader for a team, guiding architecture decisions, design reviews, and engineering best practices.

Required Skills

• 12+ years of experience in machine learning, NLP, and Generative AI systems.
• Extensive experience in MLOps, including model lifecycle management and deployment.
• Proficiency in Databricks and ML platform tools.
• Experience deploying and managing large-scale AI/ML systems.
• Knowledge of Agentic AI and modern AI frameworks.
• Familiarity with LLMs, LangChain Development, and LLM Ops practices.
• Strong understanding of cloud-based ML infrastructure and scalable architectures.
• Experience building CI/CD pipelines for machine learning workflows.
• Bachelor's degree in Computer Science, Engineering, Data Science, or a related field.

Key Skills

Education

Any Graduate