Gene Ai Architect subcontractor

2 weeks ago


Hinjawadi Pune Maharashtra, India BirlaSoft Full time

Country/Region: IN
- Requisition ID: 27502
- Work Model:

- Position Type:

- Salary Range:

- Location: INDIA - PUNE - BIRLASOFT OFFICE - HINJAWADI

**Title**:Gene AI Architect _SubContractor**:

- Description:

**Area(s) of responsibility**:
GenAI Technical Architect (Must have - Autogen,CrewAI and WrenAI)

Key Responsibilities:
Data Curation Automation: Build tools and pipelines for automated data curation, preprocessing, and augmentation to support LLM training and fine-tuning.
Cloud Integration: Design and implement solutions leveraging Azure, GCP, and AWS LLM ecosystems, ensuring seamless integration with existing cloud infrastructure.
Responsible AI: Ensure ethical AI practices by implementing Responsible AI principles, including fairness, transparency, and accountability.
RLHF and RAG: Implement Reinforcement Learning with Human Feedback (RLHF) and Retrieval-Augmented Generation (RAG) techniques to enhance model performance.
Open Source Collaboration: Leverage Hugging Face and other open-source platforms for model development, fine-tuning, and deployment.
Front-End Integration: Collaborate with front-end developers to integrate GenAI capabilities into user-friendly interfaces..

**Required Skills**:
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.
Cloud Platforms: Extensive experience with Azure, GCP, and AWS LLM ecosystems and APIs.
Fine-Tuning Techniques: Mastery of PEFT, QLoRA, LoRA, and other fine-tuning methods.
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.
RLHF and RAG: Advanced skills in Reinforcement Learning with Human Feedback and Retrieval-Augmented Generation.
Modular RAG: Deep understanding of Modular RAG architectures and their implementation.
Hugging Face: Proficiency in using Hugging Face and similar open-source platforms for model development.
Front-End Integration: Knowledge of front-end technologies to enable seamless integration of GenAI capabilities.
SDLC and DevSecOps: Strong understanding of secure software development lifecycle and DevSecOps practices for LLMs.


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