Lead the architecture and delivery of enterprise-grade Generative AI and Agentic AI solutions. This role involves translating complex business requirements into scalable, secure, and production-ready AI platforms. You will own the technical vision from the initial discovery and proof-of-concept stages through to production deployment and ongoing optimization, ensuring robust and efficient AI systems.

Join Iris Software, recognized as one of India's Top 25 Best Workplaces in the IT industry. We offer an award-winning culture that values talent and ambition, providing a launchpad for professional growth. With over 4,300 associates across India, USA, and Canada, we empower clients in financial services, healthcare, transportation, and professional services with cutting-edge technology transformations.

Design comprehensive end-to-end architectures for GenAI, RAG, Agentic AI, and multi-agent systems. Define solution patterns encompassing LLMs, embeddings, vector databases, orchestration, APIs, and enterprise integrations. Architect AI agents with advanced capabilities like tool/function calling, workflow orchestration, memory management, and Human-in-the-Loop integrations. Evaluate and select optimal foundation models, AI platforms, and architectural patterns considering quality, latency, security, and cost. Design solutions leveraging leading cloud AI platforms such as Azure OpenAI/Azure AI Foundry, Amazon Bedrock/AgentCore, or Google Vertex AI. Establish robust LLMOps, evaluation frameworks, observability, guardrails, and Responsible AI practices. Define scalable cloud-native deployment architectures using APIs, containers, Kubernetes, serverless, and event-driven patterns. Ensure adherence to enterprise requirements for security, privacy, resilience, scalability, compliance, and cost optimization. Lead architecture workshops, technical design reviews, PoCs, and solution demonstrations. Create reusable reference architectures, technical standards, and accelerators for broader GenAI adoption.

Possess strong expertise in Generative AI, Large Language Models (LLMs), Agentic AI, and AI solution architecture. Demonstrate hands-on experience with Retrieval-Augmented Generation (RAG), embeddings, vector search, and semantic retrieval. Showcase experience with agent frameworks like LangGraph, LangChain, Microsoft Agent Framework, Semantic Kernel, or Bedrock Agents/AgentCore. Exhibit a deep understanding of multi-agent orchestration, tool calling, context engineering, memory, and Human-in-the-Loop patterns. Have experience with at least one major cloud AI ecosystem: Azure, AWS, or GCP. Demonstrate strong proficiency in Python, REST APIs, microservices, and distributed/cloud-native architectures. Bring experience in LLM evaluation, tracing, monitoring, prompt/model versioning, and LLMOps/MLOps. Understand AI security, guardrails, Responsible AI principles, identity/access management, and data governance. Possess the ability to effectively assess architectural trade-offs concerning accuracy, latency, scalability, reliability, and cost.