> Markdown version of [/jobs/ext/4599828-enterprise-data-architect](https://www.wearedevelopers.com/jobs/ext/4599828-enterprise-data-architect). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Enterprise Data Architect - **Company:** Tata Consultancy Services Limited - **Location:** San Jose, CA, United States - **Salary:** $170,000 - $215,000 - **Contract:** Permanent contract - **Skills:** LangGraph Framework, Java (Programming Language), Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Software Applications, Microsoft Azure, Business Software, Cloud Computing, Continuous Integration, Python (Programming Language), Machine Learning, Search Technologies, Enterprise Data Management, Retrieval-Augmented Generation, Large Language Models, Prompt Engineering, Generative AI, Backend, Agentic-AI, Fastapi, Data Layers, Containerization, Restful APIs - **Published:** November 6, 2026 - **Apply:** https://jobs.localjobnetwork.com/apply/add/88793905/1 ## About the Role Generative AI & LLMs - hands-on experience integrating Large Language Models into applications. Agentic AI - experience building AI agents and agent-driven workflows LangGraph & MCP - hands-on experience with agentic frameworks/protocols Prompt Engineering - experience designing and optimizing prompts for LLM-based applications Vector Databases & Embeddings - experience with semantic search and AI application data layers REST APIs / FastAPI - ability to develop and integrate backend services and AI APIs Cloud - hands-on experience with AWS, Azure, or GCP; for your AWS-specific position, AWS should be mandatory Devel op AI-powered applications using GenAI, LLMs, RAG, and Agentic AI. Build scalable applications and APIs using Python/Java and modern full-stack technologies. Integrate AI models with enterprise data and business applications. Deploy solutions on AWS/Azure/GCP using CI/CD and cloud-native technologies. Implement AI security, governance, monitoring, and Responsible AI practices. Collaborate with business and engineering teams to automate processes and improve productivity