Generative AI Engineer

About Birlasoft

Birlasoft, a powerhouse where domain expertise, enterprise solutions, and digital technologies converge to redefine business processes. We take pride in our consultative and design thinking approach, driving societal progress by enabling our customers to run businesses with unmatched efficiency and innovation. As part of the CK Birla Group, a multibillion-dollar enterprise, we boast a 12,500+ professional team committed to upholding the Group's 162-year legacy. Our core values prioritize Diversity, Equity, and Inclusion (DEI) initiatives, along with Corporate Sustainable Responsibility (CSR) activities, demonstrating our dedication to building inclusive and sustainable communities. Join us in shaping a future where technology seamlessly aligns with purpose.

About the Job

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.

Job Title: GEN AI Develope

rExperience Required - 6-8 Year

sNotice Period – 0-30 Days onl

yLocation - Noida/Pune/Chennai/HYD/Bengaluru/Mumba

iMust Have - Autogen,CrewAI and WrenAI (either of these two

)

1. JDPython Programming: Deep expertise in Python for building GenAI applications and automation tool
2. s.Productionization of GenAI application beyond PoCs – Using scale frameworks and tools such as Pylint,Pyrit et
3. c.LLM Frameworks: Proficiency in frameworks like Autogen, Crew.ai, LangGraph, LlamaIndex, and LangChai
4. n.Large-Scale Data Handling & Architecture: Design and implement architectures for handling large-scale structured and unstructured dat
5. a.Multi-Modal LLM Applications: Familiarity with text chat completion, vision, and speech model
6. s.Fine-tune SLM(Small Language Model) for domain specific data and use case
7. s.Prompt injection fallback and RCE tools such as Pyrit and HAX toolkit et
8. c.Anti-hallucination and anti-gibberish tools such as Bleu et
9. c.Front-End Technologies: Strong knowledge of React, Streamlit, AG Grid, and JavaScript for front-end developmen
10. t.Cloud Platforms: Extensive experience with Azure, GCP, and AWS for deploying and managing GenAI applications. (any two cloud exp
11. .)Fine-Tuning Techniques: Mastery of PEFT, QLoRA, LoRA, and other fine-tuning methods. (any one is fin
12. e)LLMOps: Strong knowledge of LLMOps practices for model deployment, monitoring, and managemen
13. t.Responsible AI: Expertise in implementing ethical AI practices and ensuring compliance with regulation
14. s.RAG and Modular RAG: Advanced skills in Retrieval-Augmented Generation and Modular RAG architecture
15. s.Data Modernization: Expertise in modernizing and transforming data for GenAI application
16. s.OCR and Document Intelligence: Proficiency in OCR and document intelligence using cloud-based tool
17. s.API Integration: Experience with REST, SOAP, and other protocols for API integratio
18. n.Data Curation: Expertise in building automated data curation and preprocessing pipelin