Job Title : Lead Data Scientist - Generative AI (GenAI)

About Ganit :

At Ganit, analytics is our playing field. We're not just data geekswe're a family of scientists, refiners, and artisans. We manage data from the source and transform it into meaningful business insights, helping enterprises build state-of-the-art AI and analytics solutions that drive impactful decisions.

Founded in 2017, Ganit is a leading Data Science and Analytics consulting company with a team of 150+ passionate data scientists, analysts, and engineers. Our mission is to maximize decision velocity and minimize decision risk by enabling organizations to become truly data-driven.

At Ganit, innovation, collaboration, and continuous learning are at the core of everything we do.

Experience : 8 - 12 Years

Location : Bangalore / Mumbai / Chennai / Gurgaon

Role Overview :

We are looking for a highly motivated Lead Data Scientist - Generative AI to lead the design, development, and deployment of enterprise-grade GenAI solutions. The ideal candidate will possess strong expertise in Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Agentic AI, Prompt Engineering, and modern AI frameworks.

This role requires a blend of technical excellence, client-facing consulting experience, solution architecture capabilities, and team leadership. You will work closely with clients, business stakeholders, data engineers, and product teams to solve complex business problems using cutting-edge AI technologies.

Key Responsibilities :

Client Consulting & Solution Design :

- Engage with enterprise clients to understand business challenges and identify opportunities to leverage Generative AI.

- Translate business requirements into scalable AI solutions and technical architectures.

- Present AI strategies, solution approaches, and proof-of-concepts to senior stakeholders.

Generative AI Development :

- Design, develop, and deploy production-ready GenAI applications.

- Build solutions using Large Language Models (OpenAI, Claude, Gemini, Llama, Mistral, etc.).

- Develop Retrieval-Augmented Generation (RAG) pipelines using vector databases.

- Implement AI Agents and multi-agent workflows using modern orchestration frameworks.

- Optimize prompts, context management, and model performance.

Machine Learning & Data Science :

- Develop predictive models and ML solutions where traditional ML complements GenAI.

- Perform feature engineering, model evaluation, experimentation, and performance tuning.

- Apply statistical techniques to solve complex business problems.

Engineering & Deployment :

- Build scalable AI services using Python and modern software engineering practices.

- Deploy AI models on cloud platforms such as AWS, Azure, or GCP.

- Develop APIs and integrate AI capabilities into enterprise applications.

- Monitor model performance, cost optimization, and production reliability.

Collaboration :

- Partner with Product Managers, Solution Architects, Data Engineers, and Software Engineers to deliver end-to-end AI solutions.

- Collaborate with cross-functional teams throughout the project lifecycle.

Leadership & Mentorship :

- Lead and mentor a team of Data Scientists and AI Engineers.

- Conduct technical reviews and establish best practices.

- Guide junior team members in model development, experimentation, and deployment.

Research & Innovation :

- Stay current with advancements in LLMs, multimodal AI, Agentic AI, and emerging GenAI technologies.

- Evaluate new models, frameworks, and open-source tools.

- Drive innovation through internal accelerators, reusable assets, and thought leadership.

Preferred Qualifications :

- Bachelor's or Master's degree in Computer Science, Data Science, Artificial Intelligence, Statistics, Mathematics, or a related field.

- 7 - 12 years of experience in Data Science or Machine Learning.

- 2+ years of hands-on experience building production-grade Generative AI applications.

- Experience in consulting or client-facing roles.

- Strong communication and stakeholder management skills.

- Experience leading technical teams and mentoring data scientists.