Top Skills:
AiApi IntegrationArchitectureAutomationAwsAzureBuild ToolCloudContinuous IntegrationData HandlingGcpOcrOptical Character RecognitionPipelinePythonReactRestSoapTechnical DocumentationUnstructured DataUser Interface
Area(s) of responsibility
- 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.
Sr Technical Lead-App Development
Birlasoft
linkedin
Pune/Pimpri-Chinchwad Area
5-10 years Not Disclosed
Full time
04 May 2026
Top Skills:
AiApi IntegrationArchitectureAutomationAwsAzureBuild ToolCloudContinuous IntegrationData HandlingGcpOcrOptical Character RecognitionPipelinePythonReactRestSoapTechnical DocumentationUnstructured DataUser Interface
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Job Description
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Area(s) of responsibility
- 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.