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Country/Region:  IN
Requisition ID:  31071
Work Model: 
Position Type: 
Salary Range: 
Location:  INDIA - PUNE - BIRLASOFT OFFICE - HINJAWADI

Title:  Technical Lead-App Development

Description: 

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. 

Shape 

Key Responsibilities: 

  1. Application Development: Build GenAI applications from scratch using frameworks like Autogen, Crew.ai, LangGraph, LlamaIndex, and LangChain. 
  1. Python Programming: Develop high-quality, efficient, and maintainable Python code for GenAI solutions. 
  1. Large-Scale Data Handling & Architecture: Design and implement architectures for handling large-scale structured and unstructured data. 
  1. Multi-Modal LLM Applications: Familiarity with text chat completion, vision, and speech models. 
  1. Fine-tune SLM(Small Language Model) for domain specific data and use cases. 
  1. Front-End Integration: Implement user interfaces using front-end technologies like React, Streamlit, and AG Grid, ensuring seamless integration with GenAI backends. 
  1. Data Modernization and Transformation: Design and implement data modernization and transformation pipelines to support GenAI applications. 
  1. OCR and Document Intelligence: Develop solutions for Optical Character Recognition (OCR) and document intelligence using cloud-based tools. 
  1. API Integration: Use REST, SOAP, and other protocols to integrate APIs for data ingestion, processing, and output delivery. 
  1. Cloud Platform Expertise: Leverage Azure, GCP, and AWS for deploying and managing GenAI applications. 
  1. Fine-Tuning LLMs: Apply fine-tuning techniques such as PEFT, QLoRA, and LoRA to optimize LLMs for specific use cases. 
  1. LLMOps Implementation: Set up and manage LLMOps pipelines for continuous integration, deployment, and monitoring. 
  1. Responsible AI Practices: Ensure ethical AI practices are embedded in the development process. 
  1. RAG and Modular RAG: Implement Retrieval-Augmented Generation (RAG) and Modular RAG architectures for enhanced model performance. 
  1. Data Curation Automation: Build tools and pipelines for automated data curation and preprocessing. 
  1. Technical Documentation: Create detailed technical documentation for developed applications and processes. 
  1. Collaboration: Work closely with cross-functional teams, including data scientists, engineers, and product managers, to deliver high-impact solutions. 
  1. Mentorship: Guide and mentor junior developers, fostering a culture of technical excellence and innovation. 

Shape 

Required Skills : 

  1. Python Programming: Deep expertise in Python for building GenAI applications and automation tools. 
  1. Productionization of GenAI application beyond PoCs – Using scale frameworks and tools such as Pylint,Pyrit etc. 
  1. LLM Frameworks: Proficiency in frameworks like Autogen, Crew.ai, LangGraph, LlamaIndex, and LangChain. 
  1. Large-Scale Data Handling & Architecture: Design and implement architectures for handling large-scale structured and unstructured data. 
  1. Multi-Modal LLM Applications: Familiarity with text chat completion, vision, and speech models. 
  1. Fine-tune SLM(Small Language Model) for domain specific data and use cases. 
  1. Prompt injection fallback and RCE tools such as Pyrit and HAX toolkit etc. 
  1. Anti-hallucination and anti-gibberish tools such as Bleu etc. 
  1. Front-End Technologies: Strong knowledge of React, Streamlit, AG Grid, and JavaScript for front-end development. 
  1. Cloud Platforms: Extensive experience with Azure, GCP, and AWS for deploying and managing GenAI applications. 
  1. Fine-Tuning Techniques: Mastery of PEFT, QLoRA, LoRA, and other fine-tuning methods. 
  1. LLMOps: Strong knowledge of LLMOps practices for model deployment, monitoring, and management. 
  1. Responsible AI: Expertise in implementing ethical AI practices and ensuring compliance with regulations. 
  1. RAG and Modular RAG: Advanced skills in Retrieval-Augmented Generation and Modular RAG architectures. 
  1. Data Modernization: Expertise in modernizing and transforming data for GenAI applications. 
  1. OCR and Document Intelligence: Proficiency in OCR and document intelligence using cloud-based tools. 
  1. API Integration: Experience with REST, SOAP, and other protocols for API integration. 
  1. Data Curation: Expertise in building automated data curation and preprocessing pipelines. 
  1. Technical Documentation: Ability to create clear and comprehensive technical documentation. 
  1. Collaboration and Communication: Strong collaboration and communication skills to work effectively with cross-functional teams. 
  1. Mentorship: Proven ability to mentor junior developers and foster a culture of technical excellence. 

 

 

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