Join Iris Software, a top-tier IT services company recognized among India's Best Workplaces, and contribute to shaping the future of Generative AI. We are seeking a visionary Lead to define and drive our enterprise Generative AI strategy. At Iris, you'll own your career journey and work on cutting-edge projects in a culture that values innovation and employee growth. Be part of a global team making a real impact across diverse industries.

Our vision is to be the most trusted technology partner for our clients, offering transformative solutions using the latest technologies. With a presence in India, the U.S.A., and Canada, we specialize in complex application engineering, data analytics, cloud, DevOps, and AI/ML solutions. We foster an environment where your potential is recognized, and your contributions lead to significant business value and professional development.

Define and implement the enterprise Generative AI strategy, aligning with business objectives and AI transformation goals.

Establish robust AI engineering standards, governance frameworks, and best practices for AI solution delivery.

Architect enterprise-scale AI solutions leveraging platforms like Amazon Bedrock, Azure OpenAI Service, and AI Search Index.

Develop standards for Retrieval-Augmented Generation (RAG), Graph RAG, Agentic AI systems, and intelligent automation.

Promote the adoption of AI agents, tool-calling frameworks, and autonomous workflows across business units.

Drive the implementation of multi-agent architectures and Human-in-the-Loop (HITL) governance.

Set governance standards for model customization, fine-tuning, prompt engineering, and AI solution lifecycle management.

Define standards for prompt engineering pipelines, vector databases, semantic search, and AI agent orchestration using LangChain and LangGraph.

Establish comprehensive AI engineering standards for secure API design, deployment, observability, and operational resilience.

Oversee cloud integration and deployment standards for scalable AI applications.

Lead architecture reviews to ensure AI solutions meet scalability, reliability, and business value objectives.

Guide teams on GenAI architecture, agentic systems, and AI governance best practices.

Identify and mitigate AI-related risks and architectural limitations.

Demonstrated leadership in driving AI engineering excellence and accountability across programs.

Proven ability to collaborate effectively with diverse teams and business stakeholders for seamless project delivery.

Mandatory competencies include a deep understanding of Agentic AI Systems, Advanced GenAI & Agentic Framework Concepts, Cloud Application Integration & Deployment, AI Search Index, AI Agents & Tool Calling, and LangChain.

Experience with leading enterprise-scale AI architecture design using platforms such as Amazon Bedrock, Azure OpenAI Service, Azure AI Foundry, Grog, or AI Search Index is essential.

Expertise in defining enterprise standards for Retrieval-Augmented Generation (RAG), Graph RAG, Agentic AI systems, and intelligent automation architectures is required.

Familiarity with driving adoption of AI agents, tool-calling frameworks, and autonomous workflow solutions is expected.

Knowledge of enterprise multi-agent architectures, Human-in-the-Loop (HITL) governance, and scalable AI engineering patterns is crucial.

Ability to establish governance standards for model customization, fine-tuning, prompt engineering, retrieval quality, and AI solution lifecycle management is necessary.

Proficiency in defining enterprise standards for prompt engineering pipelines, vector databases, semantic search, Retrieval-Augmented Generation, Model Context Protocol (MCP), and AI agent orchestration frameworks is key.

Experience in defining architecture patterns and engineering standards using LangChain, LangGraph, and related workflow orchestration frameworks is required.

Strong understanding of enterprise AI engineering standards covering secure API design, authentication, authorization, asynchronous processing, scheduling, scalability, deployment, observability, and operational resilience is essential.

Familiarity with establishing cloud integration and deployment standards for scalable and secure AI-enabled applications is expected.