AI Architect – Generative AI | Agentic AI | RAG
Location: Bangalore (Hybrid)
Employment Type: Full-Time
Experience: 6–8 Years
Notice Period: Immediate Joiners Only
Job Summary
Our client is seeking a highly skilled AI Architect with 6–8 years of experience to lead the design, architecture, and implementation of enterprise-scale Artificial Intelligence solutions. This role requires deep expertise in Generative AI, Agentic AI, Retrieval-Augmented Generation (RAG), GraphRAG, Vector Databases, Semantic Search, and Knowledge Management.
The ideal candidate will have a strong background in architecting scalable AI platforms, integrating Large Language Models (LLMs) into enterprise applications, and delivering production-ready AI solutions on Microsoft Azure.
The AI Architect will work closely with business stakeholders, product owners, data engineers, cloud architects, and development teams to define AI strategies, build intelligent knowledge retrieval systems, establish AI governance, and drive the adoption of responsible AI practices. The successful candidate should possess excellent solution architecture skills, strong programming expertise in Python, and hands-on experience with modern AI frameworks such as LangChain, LangGraph, and Semantic Kernel.
Key Responsibilities
- Lead the architecture and technical design of enterprise-wide Artificial Intelligence, Generative AI, and Agentic AI solutions that align with business objectives and digital transformation initiatives.
- Design and implement scalable Retrieval-Augmented Generation (RAG) and GraphRAG architectures to enable intelligent knowledge retrieval from structured and unstructured enterprise data sources.
- Architect AI-powered applications using Large Language Models (LLMs), embeddings, prompt engineering, AI agents, function calling, and orchestration frameworks such as LangChain, LangGraph, and Semantic Kernel.
- Develop enterprise semantic search and knowledge management solutions by integrating vector databases such as Pinecone, Weaviate, or Milvus to enhance information discovery and contextual intelligence.
- Define AI platform architecture, including MLOps and LLMOps best practices for model deployment, monitoring, versioning, lifecycle management, observability, and continuous improvement.
- Collaborate with business leaders, solution architects, product managers, data engineers, and cloud teams to understand business challenges and translate them into scalable AI-driven solutions.
- Design secure, reliable, and scalable AI systems leveraging Microsoft Azure AI Services, ensuring seamless integration with existing enterprise applications and cloud infrastructure.
- Establish enterprise AI governance frameworks by implementing responsible AI principles, security controls, compliance standards, data privacy policies, model explainability, and risk management practices.
- Design APIs and enterprise integration patterns to enable AI services to interact efficiently with business applications, data platforms, and third-party systems.
- Evaluate emerging AI technologies, frameworks, and industry best practices, providing recommendations for continuous innovation and enterprise AI adoption.
- Provide technical leadership and architectural guidance to cross-functional development teams, conducting design reviews, mentoring engineers, and ensuring adherence to enterprise architecture standards.
- Optimize AI solution performance by improving inference efficiency, scalability, latency, cost optimization, and overall system reliability.
- Support AI solution deployment into production environments, troubleshoot complex technical issues, and ensure high availability and operational excellence.
Qualifications
- Bachelor's or Master's degree in Computer Science, Engineering, Data Science, Artificial Intelligence, or a related discipline.
- 6–8 years of experience in Solution Architecture, AI/ML, Data Engineering, or Cloud Architecture.
- Demonstrated experience designing and implementing enterprise-scale Generative AI and AI platform solutions.
- Strong understanding of enterprise architecture, cloud-native applications, AI security, and modern software engineering practices.
- Excellent communication and stakeholder management skills with the ability to engage both technical and business teams.
