Generative AI Architect- Manager

Overview

As a Generative AI Architect, you will shape client solutions within the Data and Analytics Engineering practice by using data science methods to design and develop generative AI capabilities. As a Manager, you will lead teams, coach and develop people, manage workstreams, support stakeholder discussions, and guide solution design across complex client needs.

What you'll do

• Architect end-to-end enterprise GenAI solutions focused on agentic system designs, including multi-agent orchestration, autonomous task execution, tool-use chains, LLM integration, and RAG pipelines.
• Define and enforce agentic design standards covering agent communication protocols, intelligent task routing, lifecycle management, shared tool registries, and orchestration framework selection.
• Align technology choices with clients’ preferred cloud platforms and infrastructure, ensuring compliance, cost efficiency, portability, and integration with existing governance policies and services.
• Collaborate with business stakeholders to translate objectives into agentic AI requirements, defining agent capabilities, success metrics, and MVP scope for iterative delivery.
• Establish observability, testing, and operational standards, including telemetry, monitoring, automated regression tests, simulation environments, and production SLAs.
• Lead and mentor GenAI/Agentic AI engineering teams through architecture reviews, code-level guidance, sprint planning, and quality assurance.
• Evaluate, select, and implement agentic AI technology stacks, including vector databases, orchestration frameworks, observability tools, and AI-powered development tools.

What you'll need

• At least a Bachelor's & Master's Degree.
• At least 8 years of experience.
• Oral and written proficiency in English.

Nice to have

• Over 4 years of experience developing and scaling Generative AI projects from prototypes to enterprise production, managing throughput, latency, cost, and multi-region deployments.
• Proven expertise implementing AI interoperability protocols such as MCP (Model Context Protocol) and A2A (Agent-to-Agent) at scale.
• Experience using Azure AI Foundry, Amazon Bedrock, and Google Vertex AI to build and deploy production-grade agentic AI solutions.
• Advanced Python programming skills and hands-on experience with LangChain, LangGraph, CrewAI, and AutoGen.
• Deep understanding of Graph RAG, Vectorless RAG, Hybrid RAG, model building, fine-tuning, and evaluation.
• Strong knowledge of LLM security risks including prompt injection, jailbreaking, data exfiltration, and tool misuse, with experience designing defense-in-depth safeguards.
• Expertise in Kubernetes, Docker, serverless, and event-driven architectures for scalable deployment of agentic AI workloads.
• Relevant AI or solution architecture certifications.

Details

• Location: Bangalore.