Description

You will lead the end-to-end design and architecture of scalable Generative AI solutions, including RAG pipelines, agentic workflows, and model fine-tuning strategies.

Responsibilities

• Design, develop, and operationalize ML models by fine-tuning and personalizing existing architectures.
• Build and deploy scalable machine learning pipelines on GCP or equivalent cloud platforms, handling data warehouses and inference serving.
• Develop and iteratively refine prompts for LLMs, conducting thorough analysis to evaluate responses and improve performance.
• Clean, transform, and augment datasets, performing exploratory data analysis and feature selection to support model training.
• Write clean, high-quality, and scalable code for core components including vector search, data processing, and model evaluation.

Required Skills

• 12+ years of experience building and deploying machine learning models in production environments.
• Expertise in Python, NumPy, Pandas, and ML libraries such as 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) machine learning services and data engineering workflows.
• Ability to act as the senior-most developer, owning architecture and code quality for scalable systems.

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