Generative AI Developer

Birlasoft ( India ) Limited; Pune District; On-site; INR 1,200,000 - 1,600,000; Full time

Job summary

Birlasoft ( India ) Limited is seeking a Generative AI Developer to work on developing innovative GenAI applications. The ideal candidate will have over 4 years of experience in Python programming and strong skills in managing large-scale data architectures. You will be responsible for building applications and implementing user interfaces using modern front-end technologies.

The role requires familiarity with various LLM frameworks and the ability to work on effective AI solutions while ensuring responsible AI practices.

Qualifications

• 4+ years of experience in Python programming and application development.
• Experience with LLM frameworks like Autogen and LangChain.
• Proficient in managing large-scale data architecture.

Responsibilities

• Build GenAI applications using various frameworks.
• Develop efficient and maintainable Python code.
• Implement user interfaces with React and other front-end tools.

Skills

Python Programming

LLM Frameworks

Cloud Platforms

Front‑End Technologies

Fine‑Tuning Techniques

Responsible AI

Job description

Select how often (in days) to receive an alert:

Location: INDIA - PUNE - BIRLASOFT OFFICE - HINJAWADI

Title: Generative AI Developer

Area(s) of responsibility

Job Title: GEN AI Developer

Location – Noida/HYD/Bengaluru/Pune/Chennai/Mumbai

Experience Required – 4+ years

Key Responsibilities

• 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.
• 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.

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.
• Prompt injection fallback and RCE tools such as Pyrit and HAX toolkit etc.
• Anti‑hallucination and anti‑gibberish tools such as Bleu etc.
• 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. (any two cloud exp.)
• Fine‑Tuning Techniques: Mastery of PEFT, QLoRA, LoRA, and other fine‑tuning methods. (any one is fine)
• 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.
• OCR and Document Intelligence: Proficiency in OCR and document intelligence using cloud‑based tools.
• API Integration: Experience with REST, SOAP, and other protocols for API integration.
• Data Curation: Expertise in building automated data curation and preprocessing pipelines.
• Technical Documentation: Ability to create clear and comprehensive technical documentation.
• Collaboration and Communication: Strong collaboration and communication skills to work effectively with cross‑functional teams.