Solution Architect - Agentic AI & Data

Job Description

Solution Architect - Agentic AI & Data – Any U.S. Location

Overview

Agentic AI Architect is a client‑facing consulting role within TCS’s AI & Data business unit in the Americas. The position focuses on designing autonomous “agentic” AI solutions that autonomously make decisions, take actions, adapt to changing environments, and continuously learn. The role 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.

What You Would Be Doing

• Lead AI Architecture Design – define end‑to‑end architecture for systems incorporating autonomous agents and LLM‑based components, ensuring alignment with business goals.
• Client Workshops & Strategy – conduct workshops to understand business requirements and translate 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, troubleshooting 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 – present architectural proposals and review technical designs with client technology leaders, traveling as required.
• Ethical & Safe Design – embed ethical AI and safety considerations from the architecture stage, document and mitigate potential risks.

What Skills Are Expected

• AI/ML Solution Architecture – experience designing and architecting AI or machine learning solutions in an enterprise context.
• Deep Technical Knowledge – strong understanding of machine learning and AI techniques, especially generative AI and large language models.
• Multi‑Agent System Design – 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, able to identify risks and design mitigations.
• Cloud & Distributed Systems – deep understanding of cloud architecture and distributed system design.
• Data Management – solid understanding of data architecture for AI, including data pipelines, databases, and data lakes.
• Leadership & Communication – excellent stakeholder management skills, capable of leading discussions with C‑level executives and technical brainstorming with engineers.
• Consulting and Domain Acumen – prior client‑facing experience, adept at requirement gathering and crafting proposals.
• Problem‑Solving & Innovation – creative mindset to devise innovative AI‑agent solutions, strong analytical skills.
• Continuous Learning – demonstrated habit of ongoing learning 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.
• SaaS AI & Data Platforms – experience with leading SaaS AI & Data platforms for agentic AI development and orchestration.
• Agentic AI Tooling – exposure to frameworks and libraries for building AI agents and chains, such as LangChain and Semantic Kernel.
• Retrieval Systems – strong knowledge of search and retrieval technologies, including vector databases and semantic search.
• Cloud Services – expertise in AWS, Azure, GCP, AI services, serverless computing, containerization, and related DevOps tools.
• Programming & Scripting – proficiency in Python and at least one general‑purpose language.
• Data Platforms – knowledge of relational databases, NoSQL stores, and data processing frameworks.
• Integration & APIs – experience designing APIs, middleware, event‑driven architectures, and message brokers.
• DevOps & MLOps – familiarity with CI/CD pipelines, infrastructure as code, MLOps principles and tools.
• Security & Compliance – comfort with identity management, encryption, and compliance tools for secure AI applications.
• Collaboration & Design – proficiency with architecture and design documentation tools, including UML and agile project management tools.
• Emerging Tech – awareness of knowledge graphs and reinforcement learning frameworks.

Preferred locations: San Francisco Bay Area, Seattle, New York/New Jersey, Atlanta, Chicago, Dallas.

Salary Range: $155,550 – $247,250 per year.

Qualifications

Bachelor of Computer Science.