– Data Scientist - AI & GenAI

Role: Architect

Experience: 8–11 Years

Location: Airoli, Navi Mumbai

Role Overview

We are looking for an experienced Data Science / AI professional to lead the design, development and deployment of advanced AI solutions across Machine Learning, NLP, LLMs, Generative AI and Agentic AI.

The ideal candidate will have strong hands-on expertise in Python, Machine Learning and NLP, combined with proven experience deploying AI solutions on Azure or AWS. The role will also be responsible for establishing and implementing LLMOps best practices to manage the complete lifecycle of LLM and GenAI solutions.

This is a hands-on technical leadership role requiring the ability to take AI solutions from conceptualization and architecture through development, deployment, optimization and productionization.

Key Responsibilities

AI Solution Design & Architecture

- Architect and design AI/ML solutions from conceptualization through deployment and production.
- Translate business requirements into scalable and production-ready AI solutions.
- Define appropriate approaches for ML, NLP, LLM and GenAI-based applications.
- Evaluate emerging AI technologies and identify opportunities for intelligent automation and business value.

ML, NLP & GenAI Leadership

- Lead the development, deployment and optimization of Machine Learning and NLP models.
- Design and develop solutions using Large Language Models (LLMs) and Generative AI.
- Drive the development of Agentic AI solutions, including AI agents and intelligent workflows.
- Evaluate model performance and continuously improve accuracy, scalability and efficiency.
- Stay current with advancements in ML, NLP, LLMs, GenAI and Agentic AI.

Python & ML Pipeline Development

- Develop and maintain robust Python-based ML and AI pipelines.
- Design data-processing and model-training workflows required for AI applications.
- Work with databases to support data ingestion, processing, model development and application requirements.
- Ensure code quality, scalability,



maintainability and production readiness.

Cloud Deployment

- Deploy and manage AI/ML models and Python applications on Azure and/or AWS.
- Design solutions considering scalability, availability, performance, security and cost optimization.
- Work with cloud and engineering teams to productionize AI solutions.

LLMOps & Model Lifecycle Management

- Implement and establish LLMOps best practices for managing the complete lifecycle of LLM and GenAI solutions.
- Develop processes for model deployment, monitoring, evaluation, versioning and optimization.
- Establish appropriate practices for prompt management, model evaluation, monitoring and continuous improvement.
- Ensure AI solutions are production-ready, scalable and maintainable.

Technical Leadership & Mentoring

- Provide technical leadership to Data Science and AI/ML team members.
- Mentor team members on Machine Learning, NLP, GenAI, LLMs and Agentic AI.
- Conduct technical reviews and guide the team on architecture, development and deployment practices.
- Collaborate with Engineering, Product, Business and Technology stakeholders to deliver AI solutions.

Required Skills & Experience

Core Experience

- 8–11 years of relevant experience in Data Science / AI / Machine Learning.
- Minimum 8 years of hands-on Python programming experience.
- Minimum 8 years of experience in Machine Learning and Natural Language Processing.
- Minimum 2 years of hands-on experience with LLMs and Generative AI.
- Minimum 1 year of experience with Agentic AI.
- Minimum 8 years of experience working with databases.
- 4+ years of experience deploying ML models and/or Python applications on Azure or AWS.
- LLMOps experience is mandatory.

Technical Skills

Must Have:

Python | Machine Learning | NLP | LLM | Generative AI | Agentic AI | LLMOps | ML Pipelines | Databases | Azure / AWS

Valuable to Have:

MLOps | RAG | Vector Databases | Prompt Engineering | AI Agents | Model Evaluation | Model Monitoring | Cloud-Native AI | CI/CD

Education

Bachelor's or master’s degree in computer science, Data Science, Artificial Intelligence, Engineering, Statistics or a related technical discipline.

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