Role & responsibilities AI Solution Architect (Enterprise AI & Agentic Systems) Role Summary We are looking for a hands-on AI Solution Architect who has successfully architected and delivered at least 2 large-scale AI solutions into production. The ideal candidate started their career as a strong software engineer building enterprise-scale distributed systems and has evolved into an architect capable of leading AI platform design and implementation. This is not a research or advisory role. We need a builder who has designed, developed, and scaled AI systems that deliver measurable business outcomes. What You Will Do
- Architect and drive end-to-end delivery of enterprise AI platforms based on LLMs, RAG, and Agentic AI patterns.
- Design scalable, secure, and production-grade AI systems integrating enterprise data, applications, and workflows.
- Define architecture for agent orchestration, tool integration, memory, context management, evaluation, and observability.
- Lead architecture decisions across cloud, platform, application, and data layers.
- Partner with engineering teams to establish coding standards, deployment patterns, reliability practices, and AI governance.
- Guide development teams through architecture reviews, implementation, and production readiness.
- Communicate architecture decisions effectively to engineering leaders, business stakeholders, and clients. Must-Have Experience
- 10+ years of software engineering and architecture experience, with strong enterprise application development background.
- Personally architected and delivered 2 or more AI/GenAI solutions into production, demonstrating measurable business impact.




- Strong foundation in designing large-scale distributed systems, APIs, microservices, event-driven architectures, and cloud-native platforms.
- Hands-on experience with:
- LLM-based applications
- Retrieval Augmented Generation (RAG)
- Agentic AI architectures
- Multi-agent workflows
- AI gateways and model routing
- AI observability and evaluation frameworks
- Experience integrating AI solutions with enterprise platforms, databases, APIs, and business applications.
- Deep expertise in Python and contemporary software engineering practices.
- Strong understanding of scalability, security, performance, reliability, and operational excellence. Preferred Technical Skills
- LangGraph, ADK, CrewAI, Semantic Kernel, AutoGen, or similar agent frameworks.
- Vector databases and knowledge retrieval architectures.
- Kubernetes, container orchestration, and cloud-native deployment models.
- AWS, GCP, or Azure platform architecture.
- CI/CD, MLOps, LLMOps, AI governance, and model evaluation.
- MCP ecosystem, tool integration, and enterprise agent platforms. What Will Differentiate You
- Built AI solutions that are actively used by business users in production.
- Led architecture for platforms supporting multiple AI use cases, teams, or business units.
- Demonstrated measurable business outcomes such as productivity improvement, automation, cost reduction, or revenue impact.
- Ability to balance technical depth with business value and stakeholder communication. Interested candidate can share their resume on [email protected] Preferred candidate profile Perks and benefits

📌 Solution Architect (Bengaluru)
🏢 Capgemini
📍 Bengaluru