Description

You will lead the end-to-end architecture and deployment of scalable Generative AI systems, including RAG pipelines and agentic workflows.

This role is hybrid.

Responsibilities

• Design, fine-tune, and operationalize ML models and LLM architectures for production use.
• Build scalable ML pipelines on GCP, managing data warehouses and inference serving.
• Develop and refine prompts for LLMs, analyzing responses to improve performance.
• Clean, transform, and augment datasets, performing EDA and feature selection.
• Write clean, scalable code for vector search, data processing, and model evaluation.

Required Skills

• 12+ years of experience building and deploying ML models in production.
• Expertise in Python, NumPy, Pandas, XGBoost, TensorFlow, PyTorch, Scikit-learn, and LangChain.
• Proven hands-on experience with RAG solutions or LLM fine-tuning.
• Strong understanding of Natural Language Processing (NLP) and deep learning techniques.
• Familiarity with Google Cloud Platform (GCP) ML services and data engineering workflows.
• Ability to own architecture and code quality as the senior-most developer.

Preferred Skills

• Master’s degree in statistics, machine learning, or a related field.
• Google Cloud Certified Professional Machine Learning Engineer or TensorFlow Certified Developer.

Key Skills

Education

Master's degree