AI Data Scientist

Summary

Join Nimble Gravity's Data & AI practice as an exceptional AI/Data Scientist specializing in Generative AI (GenAI). You will design and deploy next-generation AI solutions using RAG, embeddings, and vector databases. Responsibilities include building intelligent agent-based systems, implementing MCP and multi-agent frameworks, and evaluating foundation models. You will also develop embedding pipelines, work with multi-modal data, and monitor model performance. Collaboration with data scientists, engineers, and stakeholders is crucial. Staying ahead of GenAI advancements and prototyping new methodologies are also key aspects of this role.

Requirements

• Advanced degree (Master’s or Ph.D.) in Computer Science, Data Science, Artificial Intelligence, or a related technical field
• 5+ years of experience in data science, machine learning, or applied AI, including deploying solutions at production scale
• Deep expertise in LLMs and GenAI, including RAG architectures, embeddings, vector search, and agentic orchestration frameworks (e.g., MCP, multi-agent systems)
• Strong programming skills in Python and SQL, with hands-on experience using ML/AI frameworks such as Hugging Face, LangChain, LangGraph, PyTorch, or similar toolkits
• Cloud-native expertise (Azure, AWS, or Databricks) for scalable experimentation, orchestration, and deployment of GenAI services
• Strong foundation in data science methodologies, including statistical modeling, experimentation, and data-driven problem-solving, with proven experience turning analytical insights into scalable AI solutions
• Strong analytical and problem-solving skills with a keen eye for optimizing complex data workflows
• Quick learner with a passion for staying ahead of the evolving GenAI and Agentic ecosystem

Responsibilities

• Design and deploy next-generation AI solutions leveraging Retrieval-Augmented Generation (RAG), embeddings, vector databases, and agentic architectures for real-world automation
• Build intelligent, agent-based systems for document summarization, knowledge retrieval, content generation, and transforming unstructured data (e.g., PDFs, emails, spreadsheets) into actionable insights
• Implement MCP (Model Context Protocol) and multi-agent frameworks to orchestrate complex reasoning, tool usage, and dynamic workflows across LLMs
• Evaluate, adapt, and fine-tune foundation models (LLMs) for specialized domains, ensuring scalability, reliability, and measurable business impact
• Develop and maintain embedding pipelines and semantic search capabilities, optimizing similarity search in large-scale vector databases
• Work with multi-modal data (text, images, structured/unstructured formats) to deliver rich, context-aware GenAI experiences
• Monitor and enhance model performance focusing on accuracy, latency, and robustness, including LLM evaluation frameworks for continuous improvement
• Collaborate closely with data scientists to design AI-enhanced analytical workflows, ensuring that GenAI solutions seamlessly integrate with data science models and decision-making pipelines
• Collaborate cross-functionally with engineers, product managers, and business stakeholders to define approaches and deliver production-ready AI systems
• Stay ahead of GenAI and Agentic ecosystem advancements, rapidly prototyping new methodologies and integrating state-of-the-art tools and protocols into real-world applications