[Skip to main content](#main-content) [OpenNash Jobs](https://jobs.opennash.com/) [Hiring report](/reports/hiring/) · [All Tata Consultancy Services jobs](https://jobs.opennash.com/companies/tata-consultancy-services/) · [Search jobs](/search/) Tata Consultancy Services · Hyderabad # AI Architect Hyderabad Last seen on Tata Consultancy Services’s official careers site on August 1, 2026; it may have closed since. [Apply on Tata Consultancy Services’s careers site →](https://ibegin.tcsapps.com/candidate/next/en-IN/jobs/421742J) [OpenNash Jobs](https://jobs.opennash.com/) / [Tata Consultancy Services](https://jobs.opennash.com/companies/tata-consultancy-services/) / AI Architect ## Job description Section Details / Example Content Job Requirements* 8–15 years of experience to lead the strategic design and implementation of enterprise-scale AI solutions. This role requires deep expertise in Generative AI, Agentic AI, and Responsible AI, along with a strong foundation in AI architecture, solution assessment, and cloud-native AI platforms. Key Responsibilities* Strategy & Roadmap Define and drive the AI strategy, aligning with business goals and innovation priorities. Develop and maintain the AI solution roadmap, including short-term deliverables and long-term vision. Evaluate emerging AI trends and technologies to inform strategic direction. Architecture & Design Architect end-to-end AI solutions using Gen AI, Agentic AI, LLMs, and multi-modal AI. Design intelligent agent systems using LangChain, LangGraph, Model Context Protocol (MCP), and Agent to Agent Protocols. Establish scalable and modular AI architectures that support RAG pipelines, Vector DBs, and Embeddings. Define and enforce AI governance frameworks, including Responsible AI, GuardRails, and compliance with AI Ethics & Regulations. Assessment & Optimization Conduct technical assessments of existing AI/ML systems, models, and data pipelines. Identify gaps, risks, and opportunities for modernization or enhancement. Recommend architectural improvements and integration strategies for legacy systems. Deployment & Integration Lead deployment of AI models using Docker, Kubernetes, and MLOps best practices. Integrate AI solutions with enterprise platforms and cloud-native services (Azure, AWS, GCP). Ensure performance, scalability, and security of deployed AI systems.