Technical Lead

Birlasoft

0 years

0 Lacs

Noida Uttar pradesh India

Posted:2 days ago| Platform: Foundit logo

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Skills Required

data modernization and transformation large-scale data handling cloud platform expertise fine-tune slm data curation automation mentorship fine-tuning llms responsible ai practices llmops implementation ocr and document intelligence collaboration genai applications front-end integration multi-modal llm applications rag and modular rag

Work Mode

On-site

Job Type

Full Time

Job Description

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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