Are you eager to contribute to groundbreaking AI initiatives at one of India's top workplaces? Iris Software invites experienced professionals to lead our Generative AI Fullstack efforts.

Join a dynamic, award-winning culture that champions employee growth and innovation. We are a rapidly expanding IT services firm committed to being our clients' most trusted technology partner and a premier destination for industry talent.

Our expertise spans critical application development, data analytics, cloud, DevOps, MLOps, quality engineering, and business automation, utilizing cutting-edge technologies. At Iris, your career is a journey of ownership and continuous development, supported by a culture that values your contributions and aspirations.

Define and advance the enterprise Generative AI strategy, aligning with business goals and innovation objectives. Establish robust AI engineering standards, governance frameworks, and best practices for AI solution delivery.

Design enterprise-scale AI architectures using platforms like Amazon Bedrock, Azure OpenAI Service, or AI Search Index. Define standards for Retrieval-Augmented Generation (RAG), Agentic AI systems, and intelligent automation.

Champion the adoption of AI agents, tool-calling frameworks, and autonomous workflows. Drive the implementation of multi-agent architectures and Human-in-the-Loop (HITL) governance for scalable business automation.

Set governance standards for model customization, prompt engineering, retrieval quality, and AI solution lifecycle management. Define enterprise patterns for prompt engineering, vector databases, and AI agent orchestration using frameworks like LangChain and LangGraph.

Establish comprehensive enterprise AI engineering standards covering secure API design, authentication, authorization, asynchronous processing, deployment, observability, and operational resilience. Ensure cloud integration and deployment standards support scalable and secure AI applications.

Lead architecture reviews to guarantee AI solutions meet objectives for scalability, reliability, maintainability, and business value. Guide teams on GenAI architecture, agentic systems, AI governance, and best practices for enterprise AI adoption. Identify and mitigate AI-related risks and architectural limitations.

Proven leadership and accountability in driving AI engineering excellence. Ability to collaborate effectively with diverse teams and business stakeholders to ensure seamless project delivery.

Mandatory skills include expertise in Agentic AI Systems, Advanced GenAI, Agentic Framework Concepts, Cloud Application Integration & Deployment, AI Search Index, AI Agents & Tool Calling, and LangChain. Experience with platforms such as Amazon Bedrock, Azure OpenAI Service, Azure AI Foundry, Grog, or AI Search Index is essential.

Define enterprise standards for Retrieval-Augmented Generation (RAG), Graph RAG, Agentic AI systems, and intelligent automation architectures. Drive adoption of AI agents, tool-calling frameworks, and autonomous workflow solutions across business functions.

Establish governance standards for model customization, fine-tuning, prompt engineering, retrieval quality, and AI solution lifecycle management. Define enterprise standards for prompt engineering pipelines, vector databases, semantic search, Retrieval-Augmented Generation, Model Context Protocol (MCP), and AI agent orchestration frameworks.

Define architecture patterns and engineering standards using LangChain, LangGraph, and related workflow orchestration frameworks. Establish enterprise AI engineering standards covering secure API design, authentication, authorization, asynchronous processing, scheduling, scalability, deployment, observability, and operational resilience.

Establish cloud integration and deployment standards for scalable and secure AI-enabled applications. Establish enterprise integration standards supporting databases, enterprise APIs, messaging platforms, and cloud-native AI application deployment. Lead architecture reviews and ensure AI solutions meet scalability, reliability, maintainability, explainability, and business value objectives. Guide teams on GenAI architecture, agentic systems, AI governance, and enterprise AI adoption best practices. Identify AI-related risks, governance gaps, operational challenges, and architectural limitations while defining mitigation strategies. Collaborate with various teams and leadership stakeholders to align AI initiatives with organizational objectives. Drive continuous improvement initiatives focused on AI maturity, innovation, operational effectiveness, governance, and business value realization.