Get new jobs for this search by email

Job Description:
The Agentic AI Architect is a senior-level, client-facing role within TCS's AI & Data business unit in the Americas. This position is responsible for designing next-generation AI solutions that leverage autonomous, agentic AI systems-AI that can make decisions, take actions, and adapt independently. The architect will lead the design and delivery of scalable, ethical, and industry-specific AI architectures across sectors such as BFSI, Manufacturing, Life Sciences, Telecom, Retail, and more.
Key Responsibilities:
AI Architecture Design: Define end-to-end architectures for AI systems incorporating autonomous agents and LLM-based components.
Client Engagement: Conduct workshops and consulting sessions to translate business needs into AI architecture blueprints.
Multi-Agent Orchestration: Design frameworks for multi-agent systems, including agent roles, coordination protocols, and fail-safes.
Enterprise Integration: Plan secure, scalable integration with enterprise systems (e.g., ERP, IoT, core banking).
Prompt Engineering & RAG: Implement advanced prompt engineering and retrieval-augmented generation strategies.
Technical Leadership: Guide engineering teams through prototyping and delivery, ensuring architectural integrity.
Industry-Specific Customization: Tailor AI solutions to meet compliance, personalization, or privacy needs across industries.
Emerging Tech Evaluation: Continuously assess and integrate new AI tools, frameworks, and methodologies.
Ethical AI Design: Embed responsible AI principles (e.g., transparency, fairness, security) into system architecture.
Client-Facing Delivery: Present architectural proposals, lead discovery sessions, and support critical deployment phases.
Qualifications:
8+ years of experience in AI/ML solution architecture in enterprise environments.
Strong knowledge of Generative AI, LLMs, and agentic AI frameworks (e.g., LangChain, Semantic Kernel).
Proficiency in prompt engineering and retrieval-augmented generation (RAG).
Experience with multi-agent system design and orchestration.
Deep understanding of cloud platforms (AWS, Azure, GCP) and distributed systems.
Familiarity with AI/ML frameworks (OpenAI, Hugging Face, TensorFlow, PyTorch).
Strong grasp of data architecture, vector databases (e.g., Pinecone, FAISS), and semantic search.
Proficiency in Python and at least one general-purpose language (Java, C#, Node.js).
Experience with API design, microservices, and enterprise integration patterns.
Knowledge of DevOps/MLOps tools and practices.
Understanding of responsible AI principles (e.g., SAFTI, fairness, transparency).
Strong client-facing skills and ability to lead workshops and explain AI concepts to non-technical stakeholders.
Proven ability to lead cross-functional teams and mentor junior engineers.
Demonstrated commitment to continuous learning and staying current with AI trends.