Area(s) of responsibility
About Birlasoft
Birlasoft, a global leader at the forefront of Cloud, AI, and Digital technologies, seamlessly blends domain expertise with enterprise solutions. The company’s consultative and design-thinking approach empowers societies worldwide, enhancing the efficiency and productivity of businesses. As part of the multibillion-dollar diversified CKA Birla Group, Birlasoft with its 12,000+ professionals, is committed to continuing the Group’s 170-year heritage of building sustainable communities.Below is the JD for GenAI Technical Architect
Key Responsibilities
- Design and Architecture: Create scalable and modular architecture for GenAI applications using frameworks like Autogen, Crew.ai, LangGraph, LlamaIndex, and LangChain.
- Python Development: Lead the development of Python-based GenAI applications, ensuring high-quality, maintainable, and efficient code.
- 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.
- Fine-Tuning Expertise: Apply advanced fine-tuning techniques such as PEFT, QLoRA, and LoRA to optimize LLM performance for specific use cases.
- LLMOps Implementation: Establish and manage LLMOps pipelines for continuous integration, deployment, and monitoring of LLM-based applications.
- 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.
- Modular RAG Design: Develop and optimize Modular RAG architectures for complex GenAI applications.
- Open Source Collaboration: Leverage Hugging Face and other open-source platforms for model development, fine-tuning, and deployment.
Required Skills
- Python Programming: Deep expertise in Python for building GenAI applications and automation tools.
- 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.
- 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.
