Job Description

Greetings from TCS..!!

TCS is hiring for the role Gemini Solution Architect

Experience : 10 to 20 years

Location : Pan India

Interview Mode : Virtual Interview

Role Summary:

The GCP AI, GenAI & Agentic AI Architect will be responsible for designing and shaping enterprisegrade AI, Generative AI, and Agentic AI solutions on Google Cloud Platform. The role focuses on translating business problems into scalable, secure, and governable AI architectures, supporting presales solutioning, architecture definition, and early delivery alignment. The architect acts as a technical authority across ML platforms, GenAI systems, LLM integration, and autonomous agent frameworks.

Roles & Responsibilities:

Design endtoend AI and GenAI architectures on GCP, covering data pipelines, model development, inference, orchestration, and monitoring.
Architect LLMbased applications, including RetrievalAugmented Generation (RAG), prompt orchestration, multimodel strategies, and tool/function calling.
Design Agentic AI systems, including taskoriented agents, planners, toolusing agents, and autonomous workflows.
Define AsIs / ToBe AI architectures, AI modernization roadmaps, and platform blueprints.
Strong expertise in GCP AI stack (Vertex AI, model training, deployment, inference)
Gemini Enterprise for Customer Experience (GECX). Create agentic AI solution using GECX
Lead AI/GenAI presales engagements, including discovery workshops, solution walkthroughs, and executive presentations.
Translate business use cases into practical AI, GenAI, and Agentic AI solutions with clear value articulation.

support RFPs, proposals, estimates, and AI platform solution narratives.

Architect solutions using GCP AI and data services such as Vertex AI, BigQuery, Dataflow, Dataproc, Cloud Storage, Pub/Sub, and Cloud Run.
Guide LLM integration using Google models and thirdparty LLMs, ensuring portability and extensibility.
Define model lifecycle management, MLOps, monitoring, and inference optimization.
Define guardrails for responsible AI, including bias mitigation, hallucination control, access controls, and auditability.
Design architectures for model governance, explainability, observability, and cost control.
Support enterprise frameworks for AI risk management and compliance.
Ensure AI solutions meet security, governance, compliance, and data privacy requirements.