RAG Architect(GenAI Architect)

Job Description

Roles & Responsibilities : Education and Work Experience Requirements: 5 to 8 years of experience as Data Science Architect 2 to 3 years of experience in Generative AI solution development Proven track record and experience with with GenAI technologies o Open source LLMs like Llama, Gemma, Mixtral etc o Closed source LLMs such as Open AI GPT, Azure Open AI, Claude, Gemini etc o Prompt Engineering/Tuning, RAG, RAFT, LLM finetuning such as PEFT(LoRA, QLoRA ..) o Understanding of SLMs such as Phi3,QWEN, BERT and Transformer architecture o Vector databases like Pinecone, Qdrant etc. Good knowledge of advanced statistical methods. Experience working with Text Data using transformer-based model Expertise with the following scripting languages: o Python, R, Tensorflow, Keras, Pytorch o OpenNLP, CoreNLP, WordNet, NLTK, SpaCy, Gensim, Large Language Models, Knowledge Graphs Design and implement end-to-end Generative AI solutions, ensuring seamless integration with enterprise applications and workflows. Lead technical evaluations and POCs (Proof of Concepts) to assess new AI models, frameworks, and vector databases for RAG solutions. Develop scalable indexing and retrieval strategies to optimize search and generation performance in large-scale AI systems. Implement hybrid search techniques, including dense and sparse retrieval, for enhanced information retrieval accuracy. Define and enforce architectural standards, coding best practices, and performance benchmarks for RAG-based applications. Collaborate with business leaders to identify AI-driven opportunities and develop strategic roadmaps for AI product evolution. Drive the adoption of domain-specific AI solutions by tailoring RAG architectures to industry-specific requirements. Establish feedback loops and model monitoring mechanisms to ensure continuous improvement of AI-generated outputs. Utilize MLOps best practices to automate model deployment, monitoring, and retraining pipelines for Generative AI solutions. Optimize knowledge graphs and vector embeddings to enhance contextual awareness in Graph RAG implementations. Develop strategies for handling long-context retrieval and efficient chunking mechanisms for better AI response quality. Leverage fine-tuning and reinforcement learning with human feedback (RLHF) to improve generative AI model performance. Explore and integrate federated learning techniques to enable privacy-preserving AI model training and inference. Conduct AI ethics assessments and bias mitigation strategies to ensure responsible AI development and deployment. Engage with open-source AI communities and contribute to research publications or technical blogs on RAG advancements. Participate in technical panels, conferences, and industry events to showcase AI expertise and thought leadership. Support sales and pre-sales teams by providing technical guidance, solution demonstrations, and AI-driven business value assessments. Establish AI Centers of Excellence (CoE) within the organization to drive innovation, knowledge sharing, and AI capability building. Ensure compliance with enterprise security policies, data governance regulations, and responsible AI guidelines. Develop multi-modal AI capabilities that integrate NLP, Computer Vision, and Speech Recognition for advanced generative applications. Prior experience working on Mobility, Manufacturing or Healthcare domain will be a plus. Mandatory Skills: Design, develop, test, and deploy Machine Learning models using state-of-the-art algorithms with a strong focus on language models. Strong understanding of LLMs, and asso Qualifications Educational qualification: BE/BTech or MCA Experience : 10-15 years <