Solution Architect - Agentic AI & Data

Solution Architect - Agentic AI & Data

The Agentic AI Architect is a pivotal role within TCS’s AI & Data business unit in the Americas, focusing on designing next-generation AI solutions that leverage autonomous “agentic” AI systems. These innovative systems are designed to autonomously make decisions, take actions, adapt to changing environments, and continuously learn. Tata Consultancy Services anticipates a significant shift from traditional chatbots to sophisticated multi-agent AI frameworks, where multiple agents collaborate to determine optimal actions. This client-facing consulting position involves shaping cutting-edge AI architecture across diverse industries, delivering vertical-specific solutions for domains such as BFSI, Manufacturing, Life Sciences, Telecom, Retail, Travel, and Consumer Goods. The role demands thought leadership in emerging Business Units, ensuring that Tata Consultancy Services’s AI solutions are consistently innovative, scalable, and engineered responsibly.

What You Would Be Doing

• Lead AI Architecture Design: Define end-to-end architecture for AI systems 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 complex business problems into clear AI architecture blueprints.
• Multi-Agent Framework Orchestration: Design robust frameworks for multi-agent systems, meticulously defining roles and ensuring reliable communication and effective fail-safes.
• Integration & Scalability: Outline seamless integration with existing enterprise ecosystems, prioritizing scalability, resilience, and high performance.
• Leverage Prompt Engineering & RAG: Incorporate advanced prompt engineering techniques and retrieval-augmented generation (RAG) into solution design for enhanced AI capabilities.
• Technical Leadership in Delivery: Guide engineering teams through prototyping and solution delivery, adeptly troubleshooting high-level architectural issues.
• Industry-Tailored Solutions: Customize architectural decisions to meet specific industry requirements, balancing reusability with necessary adaptations.
• Emerging Tech Evaluation: Continuously evaluate new tools and methodologies, integrating them into established architecture standards to maintain a competitive edge.
• Client Engagement & Travel: Work closely with client technology leaders, presenting architectural proposals, reviewing technical designs, and traveling as required to client sites.
• Ethical & Safe Design: Ensure ethical AI and safety considerations are deeply embedded from the architecture stage, proactively documenting and mitigating potential risks.

What Skills Are Expected

• AI/ML Solution Architecture: Extensive experience in designing and architecting complex AI or machine learning solutions within an enterprise context.
• Deep Technical Knowledge: Strong understanding of machine learning and AI techniques, with a particular emphasis on Generative AI and large language models.
• Multi-Agent System Design: Proven knowledge of multi-agent system patterns and frameworks.
• Prompt Engineering & RAG: Ability to craft effective prompts and chaining strategies for LLMs, familiar with retrieval-augmented generation methods.
• AI Ethics & Responsible AI: Strong grasp of AI ethics and safety principles, capable of identifying ethical risks and designing effective mitigations.
• Cloud & Distributed Systems: Deep understanding of cloud architecture and distributed system design.
• Data Management: Solid understanding of data architecture as it relates to AI, including data pipelines, databases, and data lakes.
• Leadership & Communication: Excellent communication and stakeholder management skills, capable of leading discussions with C-level executives and facilitating technical brainstorming with engineers.
• Consulting and Domain Acumen: Prior consulting or client-facing experience, adept at requirement gathering and crafting compelling proposals.
• Problem-Solving & Innovation: Creative mindset to devise innovative solutions leveraging AI agents, coupled with strong problem-solving skills.
• Continuous Learning: Demonstrated habit of continuous learning, staying updated via research papers, conferences, or hands-on experimentation.

Key Technology Capabilities

• AI & ML Frameworks: Familiarity with major AI/ML frameworks and services, including OpenAI GPT models, Google PaLM/Vertex AI, and Hugging Face Transformers library.
• SaaS AI & Data Platforms: Experience with leading SaaS AI & Data platforms in terms of agentic AI development, implementation, orchestration, and AI guardrails.
• Agentic AI Tooling: Exposure to frameworks and libraries for building AI agents and chains, such as LangChain and Microsoft’s Semantic Kernel.
• Retrieval Systems: Strong knowledge of search and retrieval technologies, including vector databases and semantic search.
• Cloud Services: Expertise in cloud ecosystems (AWS, Azure, GCP), including cloud AI services, serverless computing, containerization, and related DevOps tools.
• Programming & Scripting: Proficiency in programming languages commonly used for AI and integration, primarily Python and at least one general-purpose language.
• Data Platforms: Knowledge of modern data platforms, including relational databases, NoSQL stores, and data processing frameworks.
• Integration & APIs: Experience designing and using APIs and middleware, knowledge of event-driven architectures and message brokers.
• DevOps & MLOps: Familiar with CI/CD pipelines and infrastructure as code, understanding of MLOps principles and tools.
• Security & Compliance Tools: Comfort with technologies for securing AI applications, including identity and access management, encryption, and compliance tools.
• Collaboration & Design: Proficient with tools used in architecture and design documentation, including UML design tools and agile project management tools.
• Emerging Tech: Awareness of emerging tech such as knowledge graphs and reinforcement learning frameworks.

Key skills/competency

• AI Solution Architecture
• Generative AI
• Large Language Models (LLM)
• Multi-Agent Systems
• Prompt Engineering
• Retrieval-Augmented Generation (RAG)
• Cloud Architecture
• Python Programming
• Data Pipelines
• Ethical AI