Data Science Architect

Role Overview: As an experienced and innovative Generative AI Architect, your role will involve leading the design and implementation of cutting-edge GenAI solutions across various business domains. You will play a pivotal role in defining the architectural blueprint for scalable and secure systems that leverage Large Language Models (LLMs), transformers, and multimodal AI frameworks. Key Responsibilities: - Architect and deliver end-to-end GenAI solutions using LLMs (OpenAI, PaLM, Claude, Mistral, etc.) for enterprise use cases such as summarization, content generation, copilots, search, and Q&A systems. - Lead technical design and integration of foundation models into internal and external applications. - Define and enforce AI architecture standards, best practices, and reusable components. - Collaborate with Data Scientists, ML Engineers, and Product Teams to build and fine-tune models for specific business needs. - Drive prompt engineering, RAG (Retrieval-Augmented Generation), model fine-tuning, and evaluation strategies. - Work with stakeholders to identify use cases, assess feasibility, and translate requirements into scalable AI architectures. - Lead PoC and pilot development, scaling successful solutions into production. - Ensure responsible AI principles, including governance, fairness, security, privacy, and compliance. - Collaborate on deployment strategies using APIs, containers (Docker/Kubernetes), and MLOps pipelines. - Stay updated on GenAI trends, tools, and frameworks, and evangelize adoption across the organization. Qualifications Required: - Bachelor's or Masters in Computer Science, AI/ML, or related field. - 8+ years of experience in software/AI/ML architecture, including 2+ years with GenAI or LLM-based solutions. - Strong understanding of LLMs (GPT, LLaMA, PaLM, Claude, Mistral) and vector databases (e.g., FAISS, Pinecone, Weaviate). - Experience in prompt engineering, RAG pipelines, and orchestration frameworks (LangChain, LlamaIndex, Haystack). - Proficiency in Python, with hands-on experience in AI/ML libraries such as Transformers, PyTorch, Hugging Face, OpenAI SDKs. - Solid understanding of cloud platforms (AWS, GCP, Azure) and AI/ML toolkits (Vertex AI, Bedrock, SageMaker). - Familiarity with MLOps, CI/CD pipelines, and containerization technologies (Docker, K8s). (Note: Additional details of the company were not present in the provided Job Description),