Data Scientist; Generative AI

Position: Data Scientist (Generative AI)
Greetings from Tata Consultancy Service!!!

*** ROLE :
Data Scientist (Generative Artificial Intelligence)**
* JOB LOCATION :
Bangalore / Hyderabad / Chennai / Pune

EXPERIENCE REQUIREMENT : 5- 11 Years

Job Summary:

We are seeking an experienced Data Scientist with expertise in Generative AI (GenAI) to design, develop, and deploy AI models that generate high-quality content, improve business processes, and drive innovation. The ideal candidate will have a solid foundation in machine learning, deep learning, natural language processing (NLP), and experience working with transformer models like GPT, BERT, and other state-of-the-art large language models (LLMs).

You will collaborate closely with Big Data, UI, and UX teams to develop scalable AI solutions and ensure seamless model integration into production environments.

Key Responsibilities:

Generative AI Model Development

• Design, develop, and fine-tune generative AI models (e.g., GPT, BERT, LLaMA) for various business applications.

• Leverage transfer learning, prompt engineering, and model fine-tuning to improve model accuracy and efficiency.

Data Preparation and Feature Engineering

• Gather, clean, and preprocess large datasets required for training and validating AI models.

• Develop pipelines for model training, evaluation, and deployment.

• Implement feature extraction and engineering techniques to enhance model performance.

Model Evaluation and Optimization

• Conduct rigorous testing and validation of models to ensure accuracy, fairness, and robustness.

• Optimize models for latency, scalability, and efficiency in production environments.

• Continuously refine models through monitoring, retraining, and feedback loops.

Required

Skills and Experience:

• 5+ years of experience in data science, machine learning, or AI development.

• Strong expertise in Generative AI, NLP, and transformer-based models (e.g., GPT, BERT, T5).

• Proficiency in Python and libraries such as Tensor Flow, PyTorch, Hugging Face, and scikit-learn.

• Hands-on experience with fine-tuning LLMs and implementing RLHF (Reinforcement Learning with Human Feedback).

• Experience with prompt engineering and few-shot learning techniques.

• Familiarity with MLOps best practices and CI/CD pipelines for model deployment.

• Strong understanding of APIs, RESTful services, and integrating AI models into production.

• Ability to optimize models for performance, scalability, and low-latency inference.

Other Preferred

Skills:

• Experience with cloud platforms and container orchestration tools (Docker, Kubernetes).

• Exposure to multimodal AI models for text, image, and video generation.

• Experience with vector databases (FAISS, Pinecone, Milvus) for semantic search.

- • Understanding of Lang Chain, LLMOps, and building AI agents.