Agentic AI Solution Architect:

Job Summary:

We are seeking an experienced Enterprise AI / GenAI / Agentic AI Solution Architect to lead the design and implementation of enterprise-scale AI solutions. The ideal candidate will define the AI architecture roadmap, design end-to-end Generative AI applications, and build scalable AI platforms leveraging Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Agentic AI, and knowledge management systems.

Key Responsibilities:

- Define enterprise AI, Generative AI, and Agentic AI architecture, strategy, and technology roadmap.
- Design and architect end-to-end AI solutions leveraging enterprise data, knowledge bases, and document repositories.
- Build and implement Retrieval-Augmented Generation (RAG), GraphRAG, semantic search, and knowledge retrieval solutions.
- Develop AI applications using LLMs, embeddings, prompt engineering, AI agents, and orchestration frameworks.
- Architect scalable AI platforms using Azure AI services and enterprise integration patterns.
- Implement AI orchestration frameworks such as LangChain, LangGraph, Semantic Kernel, and related technologies.
- Design and optimize vector database solutions using Pinecone, Weaviate, Milvus, or similar platforms.
- Establish MLOps and LLMOps best practices for model deployment, monitoring, evaluation, and governance.
- Ensure Responsible AI, AI security, data privacy, compliance, and governance across enterprise AI solutions.
- Collaborate with business stakeholders,



data engineers, cloud architects, and development teams to deliver AI-driven business solutions.
- Evaluate emerging AI technologies and recommend architecture improvements to enhance enterprise AI capabilities.

Required Skills

- Strong expertise in Generative AI, Agentic AI, LLMs, RAG, GraphRAG, Semantic Search, and Knowledge Management.
- Hands-on experience with AI orchestration frameworks such as LangChain, LangGraph, Semantic Kernel, or similar.
- Experience with vector databases including Pinecone, Weaviate, Milvus, or Azure AI Search.
- Strong knowledge of prompt engineering, embeddings, AI agents, and enterprise AI architecture.
- Experience with Microsoft Azure AI services, Azure OpenAI, Azure AI Search, and Azure Machine Learning.
- Strong understanding of AI governance, Responsible AI, enterprise integration, and data architecture.
- Proficiency in Python, REST APIs, and scalable distributed systems.
- Experience implementing MLOps and LLMOps practices.
- Robust communication, stakeholder management, and solution design skills.

Preferred Skills

- Experience with Neo4j or knowledge graph technologies for GraphRAG implementations.
- Exposure to Docker, Kubernetes, CI/CD, and cloud-native architectures.
- Experience integrating AI solutions with enterprise applications and business workflows.
- Familiarity with monitoring, observability, and AI evaluation frameworks.

📌 Agentic AI Solution Architect (Karnataka)
🏢 Sunovaa Tech
📍 Karnataka