Solution Architect - Agentic AI & Data

Role: Solution Architect - Agentic AI & Data Location options: Any US Location Job Description Agentic AI Roles at TCS in US – Job Descriptions The Agentic AI Architect is a role within TCS’s AI & Data business unit in the Americas, focused on designing next-generation AI solutions that leverage autonomous "agentic" AI systems. These systems autonomously make decisions, take actions, adapt to changing environments, and continuously learn. TCS anticipates a shift from traditional chatbots to multi-agent AI frameworks where multiple agents collaborate to determine actions. This client-facing consulting position involves shaping AI architecture across various industries, delivering vertical-specific solutions for domains such as BFSI, Manufacturing, Life Sciences, Telecom, Retail, Travel, and Consumer Goods. The role involves thought leadership in emerging Business Units, ensuring TCS’s AI solutions are innovative, scalable, and responsibly engineered. Responsibilities Lead AI Architecture Design: Define end-to-end AI system architecture incorporating autonomous agents and LLM-based components, ensuring alignment with business goals. Client Workshops & Strategy: Conduct workshops to understand business requirements and identify opportunities for agentic AI, translating business problems into AI architecture blueprints. Multi-Agent Framework Orchestration: Design frameworks for multi-agent systems, defining roles and ensuring robust communication and fail-safes. Integration & Scalability: Outline integration with existing enterprise ecosystems, ensuring scalability and resilience. Leverage Prompt Engineering & RAG: Incorporate advanced prompt engineering techniques and retrieval-augmented generation into solution design. Technical Leadership in Delivery: Guide engineering teams through prototyping and solution delivery, troubleshoot high-level architectural issues. Industry-Tailored Solutions: Customize architectural decisions to industry-specific requirements, balancing reusability with necessary adaptations. Emerging Tech Evaluation: Continuously evaluate new tools and methodologies, integrating them into architecture standards. Client Engagement & Travel: Work closely with client technology leaders, present architectural proposals and review technical designs, with travel as required. Ethical & Safe Design: Ensure ethical AI and safety considerations are embedded from the architecture stage, document and mitigate potential risks. Skills and Qualifications Extensive experience designing and architecting AI or machine learning solutions in an enterprise context. Deep technical knowledge of machine learning and AI techniques, especially generative AI and large-language models. Knowledge of multi-agent system patterns and frameworks. Ability to craft effective prompts and chaining strategies for LLMs; familiarity with retrieval-augmented generation methods. Strong grasp of AI ethics and responsible AI principles, able to identify ethical risks and design mitigations. Deep understanding of cloud architecture and distributed system design. Solid understanding of data architecture for AI, including data pipelines, databases, and data lakes. Excellent communication and stakeholder management skills, capable of leading discussions with C-level executives and technical brainstorming with engineers. Prior consulting or client-facing experience; adept at requirement gathering and crafting proposals. Creative mindset to devise innovative solutions leveraging AI agents, strong problem-solving skills. Demonstrated habit of continuous learning, staying updated via research papers, conferences, or hands-on experimentation. Key Technology Capabilities Major AI/ML frameworks and services: OpenAI GPT models, Google PaLM/Vertex AI, Hugging Face Transformers library. Experience with leading SaaS AI & Data platforms in agentic AI development, implementation, orchestration, AI guardrails. Frameworks and libraries for building AI agents and chains, such as LangChain and Microsoft’s Semantic Kernel. Retrieval technologies: vector databases and semantic search. Cloud ecosystems: AWS, Azure, GCP; cloud AI services, serverless computing, containerization, related DevOps tools. Programming languages: Python and at least one general-purpose language. Modern data platforms: relational databases, NoSQL stores, data processing frameworks. API design and middleware; event-driven architectures and message brokers. CI/CD pipelines and infrastructure as code; understanding of MLOps principles and tools. Security and compliance tools: identity and access management, encryption, compliance. Architecture and design documentation tools: UML design tools and agile project management tools. Emerging technologies such as knowledge graphs and reinforcement learning frameworks. Preferred Locations San Francisco Bay Area, Seattle, New York/ New Jersey, Atlanta, Chicago, Dallas, and any US location. Salary $155,550 – $247,250 a year. Job ID 408325 #J-18808-Ljbffr