Generative AI Lead

Birlasoft; Hyderabad; Hybrid; INR 600,000 - 900,000; Full time

Job summary

Birlasoft is seeking a DataScience GEN AI Lead to drive GenAI initiatives across multiple domains. The role emphasizes hands-on Python development, LLM integration, and scalable architecture for enterprise-grade solutions.

You'll work hybrid across engaged teams, leveraging cloud platforms (Azure, AWS, GCP) and modern MML/LLMOps practices to deliver robust GenAI applications with responsible AI considerations.

Qualifications

• Hands-on Python programming for GenAI applications.
• Experience in productionizing GenAI apps with scale frameworks.
• Proficiency with large-scale data handling and mult-modal AI models.

Responsibilities

• Develop and deploy GenAI applications using current LLMs and frameworks.
• Architect data pipelines and integrations for scalable GenAI solutions.
• Lead cross-functional collaboration including front-end and cloud deployment.

Skills

Python

GenAI

LLM Frameworks

Data Architecture

RAG

Modular RAG

Front-End Technologies

Cloud Platforms

PEFT/LoRA

LLMOps

Responsible AI

API Integration

Multi-Modal AI

Data Modernization

React/Streamlit/JavaScript

Job description

Job Description

Area(s) of responsibility

• Job Title: DataScience GEN AI Lead Location: Noida/Pune/Bengaluru/Chennai/Mumbai Experience: 6-8 years Mode - Hybrid Role - Fulltime

The Technical Lead will focus on the development, implementation, and engineering of GenAI applications using the latest LLMs and frameworks. This role requires hands‑on expertise in Python programming, cloud platforms, and advanced AI techniques, along with additional skills in front‑end technologies, data modernization, and API integration. The Technical Lead will be responsible for building applications from the ground up, ensuring robust, scalable, and efficient solutions.

Required Skills

• Python Programming: Deep expertise in Python for building GenAI applications and automation tools.
• Productionization of GenAI application beyond PoCs – Using scale frameworks and tools such as Pylint,Pyrit etc.
• LLM Frameworks: Proficiency in frameworks like Autogen, Crew.ai, LangGraph, LlamaIndex, and LangChain.
• Large-Scale Data Handling & Architecture: Design and implement architectures for handling large-scale structured and unstructured data.
• Multi-Modal LLM Applications: Familiarity with text chat completion, vision, and speech models.
• Fine-tune SLM(Small Language Model) for domain specific data and use cases.
• Front-End Technologies: Strong knowledge of React, Streamlit, AG Grid, and JavaScript for front-end development.
• Cloud Platforms: Extensive experience with Azure, GCP, and AWS for deploying and managing GenAI applications.
• Fine-Tuning Techniques: Mastery of PEFT, QLoRA, LoRA, and other fine-tuning methods.
• LLMOps: Strong knowledge of LLMOps practices for model deployment, monitoring, and management.
• Responsible AI: Expertise in implementing ethical AI practices and ensuring compliance with regulations.
• RAG and Modular RAG: Advanced skills in Retrieval-Augmented Generation and Modular RAG architectures.
• Data Modernization: Expertise in modernizing and transforming data for GenAI applications.
• API Integration: Experience with REST, SOAP, and other protocols for API integration.