Description

You will bridge data science and software engineering, taking ML models from concept to production using Generative AI and traditional ML.

Responsibilities

• Design, develop, and fine-tune Generative AI solutions using models like Gemini for tasks like summarization and report generation.
• Architect and implement Retrieval-Augmented Generation (RAG) systems to ensure context-aware responses.
• Design and deploy various ML models (classification, regression, etc.) on Google Cloud Platform.
• Build and maintain automated MLOps pipelines covering data prep, training, and deployment using tools like Vertex AI.
• Partner with stakeholders to frame problem statements and deliver integrated AI/ML solutions.

Required Skills

• 5+ years of professional experience building and deploying ML models in production.
• Advanced proficiency in Python and core libraries (PyTorch, SciKit-learn, Pandas).
• Advanced proficiency in SQL for complex data manipulation.
• Demonstrable experience in prompt engineering or fine-tuning Large Language Models (LLMs).
• Hands-on experience with Google Cloud Platform (GCP).
• Solid understanding of MLOps principles and tools (Vertex AI, CI/CD).
• Experience implementing RAG architectures.

Key Skills

Education

Any Graduate