Gemini Enterprise Agent Platform Solution Architects

Job Title: GCP AI, GenAI & Agentic AI Architect

Location: India

Role Type: Solution Architecture | AI Platforms | PreSales & Transformation

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.

Key Responsibilities

1. AI, GenAI & Agentic Architect

 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

2. PreSales & Client Advisory

 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.

3. Technology Leadership

 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.

4. AI Governance, Risk & Responsible AI

 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.

Required Experience & Skills

Experience

 8+ years in data, ML, or cloud architecture roles

 3+ years in AI/ML or GenAI solution design

 Experience in clientfacing or presales solutioning role

Consulting & Commercial Skills

 Ability to articulate business value of AI and GenAI solutions

 Strong communication skills with technical and executive stakeholders

Certifications (Preferred)

 Google Cloud Professional Machine Learning Engineer

 Google Cloud Professional Cloud Architect

 GenAIfocused certifications are a plus