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Country/Region:  IN
Requisition ID:  24092
Work Model: 
Position Type: 
Salary Range: 
Location:  INDIA - NOIDA- BIRLASOFT OFFICE

Title:  Technical Lead-App Development

Description: 

Long Description

GenAI Technical Architect 
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. 

Required Skills :
1.    Python Programming: Deep expertise in Python for building GenAI applications and automation tools.
2.    Productionization of GenAI application beyond PoCs – Using scale frameworks and tools such as Pylint,Pyrit etc.
3.    LLM Frameworks: Proficiency in frameworks like Autogen, Crew.ai, LangGraph, LlamaIndex, and LangChain.
4.    Large-Scale Data Handling & Architecture: Design and implement architectures for handling large-scale structured and unstructured data.
5.    Multi-Modal LLM Applications: Familiarity with text chat completion, vision, and speech models.
6.    Fine-tune SLM(Small Language Model) for domain specific data and use cases.
7.    Prompt injection fallback and RCE tools such as Pyrit and HAX toolkit etc.
8.    Anti-hallucination and anti-gibberish tools such as Bleu etc.
9.    Front-End Technologies: Strong knowledge of React, Streamlit, AG Grid, and JavaScript for front-end development.
10.    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 fine)
12.    LLMOps: Strong knowledge of LLMOps practices for model deployment, monitoring, and management.
13.    Responsible AI: Expertise in implementing ethical AI practices and ensuring compliance with regulations.
14.    RAG and Modular RAG: Advanced skills in Retrieval-Augmented Generation and Modular RAG architectures.
15.    Data Modernization: Expertise in modernizing and transforming data for GenAI applications.
16.    OCR and Document Intelligence: Proficiency in OCR and document intelligence using cloud-based tools.
17.    API Integration: Experience with REST, SOAP, and other protocols for API integration.
18.    Data Curation: Expertise in building automated data curation and preprocessing pipelines.

Must have  - Autogen,CrewAI and WrenAI either of these two

GenAI Technical Architect 
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. 

Required Skills :
1.    Python Programming: Deep expertise in Python for building GenAI applications and automation tools.
2.    Productionization of GenAI application beyond PoCs – Using scale frameworks and tools such as Pylint,Pyrit etc.
3.    LLM Frameworks: Proficiency in frameworks like Autogen, Crew.ai, LangGraph, LlamaIndex, and LangChain.
4.    Large-Scale Data Handling & Architecture: Design and implement architectures for handling large-scale structured and unstructured data.
5.    Multi-Modal LLM Applications: Familiarity with text chat completion, vision, and speech models.
6.    Fine-tune SLM(Small Language Model) for domain specific data and use cases.
7.    Prompt injection fallback and RCE tools such as Pyrit and HAX toolkit etc.
8.    Anti-hallucination and anti-gibberish tools such as Bleu etc.
9.    Front-End Technologies: Strong knowledge of React, Streamlit, AG Grid, and JavaScript for front-end development.
10.    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 fine)
12.    LLMOps: Strong knowledge of LLMOps practices for model deployment, monitoring, and management.
13.    Responsible AI: Expertise in implementing ethical AI practices and ensuring compliance with regulations.
14.    RAG and Modular RAG: Advanced skills in Retrieval-Augmented Generation and Modular RAG architectures.
15.    Data Modernization: Expertise in modernizing and transforming data for GenAI applications.
16.    OCR and Document Intelligence: Proficiency in OCR and document intelligence using cloud-based tools.
17.    API Integration: Experience with REST, SOAP, and other protocols for API integration.
18.    Data Curation: Expertise in building automated data curation and preprocessing pipelines.

Must have  - Autogen,CrewAI and WrenAI either of these two

Area(s) of responsibility

Key Responsibilities:
1.    Application Development: Build GenAI applications from scratch using frameworks like Autogen (applied or acquired), Crew.ai, LangGraph, LlamaIndex, and LangChain.
2.    Python Programming: Develop high-quality, efficient, and maintainable Python code for GenAI solutions.
3.    Large-Scale Data Handling & Architecture: Design and implement architectures for handling large-scale structured and unstructured data.
4.    Multi-Modal LLM Applications: Familiarity with text chat completion, vision, and speech models.
5.    Fine-tune SLM(Small Language Model) for domain specific data and use cases.
6.    Front-End Integration: Implement user interfaces using front-end technologies like React, Streamlit, and AG Grid, ensuring seamless integration with GenAI backends.
7.    Data Modernization and Transformation: Design and implement data modernization and transformation pipelines to support GenAI applications.
8.    OCR and Document Intelligence: Develop solutions for Optical Character Recognition (OCR) and document intelligence using cloud-based tools.
9.    API Integration: Use REST, SOAP, and other protocols to integrate APIs for data ingestion, processing, and output delivery.
10.    Cloud Platform Expertise: Leverage Azure, GCP, and AWS for deploying and managing GenAI applications.
11.    Fine-Tuning LLMs: Apply fine-tuning techniques such as PEFT, QLoRA, and LoRA to optimize LLMs for specific use cases.
12.    LLMOps Implementation: Set up and manage LLMOps pipelines for continuous integration, deployment, and monitoring.
13.    Responsible AI Practices: Ensure ethical AI practices are embedded in the development process.
14.    RAG and Modular RAG: Implement Retrieval-Augmented Generation (RAG) and Modular RAG architectures for enhanced model performance.
15.    Data Curation Automation: Build tools and pipelines for automated data curation and preprocessing.
 

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