Generative AI Sr Lead
BirlaSoft —Bengaluru, Karnataka
- Azure
- Master's degree
- AWS

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Generative AI Sr Lead
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Python Programming: Develop high-quality, efficient, and maintainable Python code for GenAI solutions. OCR and Document Intelligence: Develop solutions for…

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Generative AI Sr Lead

BirlaSoft -
Bengaluru, Karnataka

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Job details

Qualifications

CI/CD
Azure
Law
React
Master's degree
Application development
SOAP
AWS
REST
APIs
AI
Python

Full job description

Country/Region: IN

Requisition ID: 35788

Work Model:

Position Type:

Salary Range:

Location: INDIA - BENGALURU - BIRLASOFT OFFICE

Title: Generative AI Sr Lead

Description:

Area(s) of responsibility

Job Title: GEN AI Sr Lead
Location - Noida/HYD/Bengaluru/Pune/Chennai/Mumbai
Experience Required - 8+ years Only

Application Development: Build GenAI applications from scratch using frameworks like Autogen (applied or acquired), Crew.ai, LangGraph, LlamaIndex, and LangChain.

Python Programming: Develop high-quality, efficient, and maintainable Python code for GenAI solutions.

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 Integration: Implement user interfaces using front-end technologies like React, Streamlit, and AG Grid, ensuring seamless integration with GenAI backends.

Data Modernization and Transformation: Design and implement data modernization and transformation pipelines to support GenAI applications.

OCR and Document Intelligence: Develop solutions for Optical Character Recognition (OCR) and document intelligence using cloud-based tools.

API Integration: Use REST, SOAP, and other protocols to integrate APIs for data ingestion, processing, and output delivery.

Cloud Platform Expertise: Leverage Azure, GCP, and AWS for deploying and managing GenAI applications.

Fine-Tuning LLMs: Apply fine-tuning techniques such as PEFT, QLoRA, and LoRA to optimize LLMs for specific use cases.

LLMOps Implementation: Set up and manage LLMOps pipelines for continuous integration, deployment, and monitoring.

Responsible AI Practices: Ensure ethical AI practices are embedded in the development process.

RAG and Modular RAG: Implement Retrieval-Augmented Generation (RAG) and Modular RAG architectures for enhanced model performance.

Data Curation Automation: Build tools and pipelines for automated data curation and preprocessing.

Technical Documentation: Create detailed technical documentation for developed applications and processes.

Collaboration: Work closely with cross-functional teams, including data scientists, engineers, and product managers, to deliver high-impact solutions.

Mentorship: Guide and mentor junior developers, fostering a culture of technical excellence and innovation

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