Gen AI Data Scientist

Tata Consultancy Services; Hyderabad, Chennai District, Bengaluru; On-site; INR 1,500,000 - 2,500,000; Full time

Job summary

Tata Consultancy Services in Hyderabad is seeking a GenAI specialist to design and deploy LLM-powered services for real business problems. You will focus on accuracy, efficiency, and scalability while applying retrieval-augmented generation, prompt templates, and evaluation techniques.

The role requires hands-on coding in Python, .NET or Java, plus experience with TensorFlow, PyTorch or Keras, and cloud platforms (AWS/Azure/GCP).

Qualifications

• Strong knowledge of GenAI concepts including foundation models / LLMs, embeddings, and NLP fundamentals.
• Hands-on prompt engineering and ability to evaluate and refine prompts based on output quality.
• Build Retrieval-Augmented Generation (RAG) solutions with retrieval strategy integration.
• Experience with agent / app evaluation to benchmark GenAI use-cases.
• Cloud-based GenAI platforms (AWS / Azure / GCP) for scalable solutions.
• MLOps/LLMOps practices: CI/CD, monitoring, logging, and version control.

Responsibilities

• Design, implement, and optimize GenAI applications for real business use-cases with focus on accuracy and scalability.
• Hands-on prompt engineering and evaluation of output quality.
• Build RAG solutions with retrieval and prompt orchestration.
• Evaluate model and agent performance for GenAI use-cases.

Skills

Generative AI concepts

Prompt engineering

RAG solutions

Agent evaluation

Cloud GenAI platforms

MLOps/LLMOps

Python

.NET

Java

Tools

TensorFlow

PyTorch

Keras

Job description

Job description

• Strong knowledge of Generative AI concepts including foundation models / LLMs, embeddings, and NLP fundamentals used in GenAI solutions.
• Design, implement, and optimize GenAI applications (LLM-powered services) for real business use-cases, with focus on accuracy, efficiency, and scalability.
• Hands-on prompt engineering (e.g., zero/few-shot, Chain-of-Thought, Tree-of-Thought, prompt templates) and ability to evaluate and refine prompts based on output quality.
• Build Retrieval-Augmented Generation (RAG) solutions, including retrieval strategy integration and prompt + retrieval orchestration patterns.
• Experience with agent / app evaluation practicesevaluate model and agent performance for GenAI use-cases (quality, effectiveness, benchmarking).
• Cloud-based GenAI platform experience (e.g., AWS / Azure / GCP) for building and deploying scalable GenAI solutions with high availability and performance.
• MLOps / LLMOps-aligned practices: CI/CD, monitoring, logging, and version control for deploying and operating GenAI systems in production.
• Proficiency in programming languages such as Python, .Net or Java, with experience in relevant libraries and frameworks (e.g., TensorFlow, PyTorch, Keras).