Join Iris Software, recognized as one of India's Top 25 Best Workplaces in IT. We are a rapidly growing IT services company offering a dynamic environment where you can shape your career success. Our vision is to be a premier technology partner for our clients and a platform for top professionals to reach their full potential.

With a global presence and over 4,300 associates, Iris Software empowers enterprise clients across financial services, healthcare, transportation & logistics, and professional services. We specialize in complex, mission-critical applications leveraging cutting-edge technologies like advanced application engineering, data & analytics, cloud, DevOps, MLOps, quality engineering, and business automation.

At Iris, we believe in 'Build Your Future. Own Your Journey.' We provide a culture that values your talent and ambition, offering cutting-edge projects, personalized career development, continuous learning, and mentorship. Discover our vibrant culture and the possibilities within Iris Software.

Design and develop advanced Generative AI solutions using platforms like Amazon Bedrock, Azure OpenAI Service, and AI Search Index. Define AI solution architectures and implementation strategies aligned with business and technical goals. Implement Retrieval-Augmented Generation (RAG), Graph RAG, and Agentic AI architectures for enterprise use cases.

Lead the development of intelligent AI agents, tool-calling workflows, and autonomous task execution frameworks. Design and implement multi-agent orchestration solutions using frameworks such as LangGraph, evaluating new options like AutoGen or CrewAI. Optimize prompt strategies, retrieval mechanisms, context orchestration, and response generation.

Develop prompt engineering pipelines, integrate vector databases, and create semantic search solutions. Design and implement workflows using LangChain or LangGraph for scalable AI application development. Architect scalable AI engineering solutions incorporating best practices for authentication, authorization, asynchronous processing, scheduling, multithreading, API governance, and enterprise deployment.

Lead fine-tuning and model customization initiatives. Define AI integration patterns and deployment approaches for enterprise application ecosystems, including cloud-native patterns. Establish evaluation frameworks for AI response quality, reliability, and relevance. Design Human-in-the-Loop (HITL) workflows to enhance AI quality and governance. Review AI solution designs for adherence to standards, scalability, maintainability, and responsible AI practices.

Troubleshoot complex AI workflow, retrieval, orchestration, and model behavior challenges. Mentor team members on GenAI frameworks, RAG architectures, agentic systems, and AI engineering best practices. Drive continuous improvement in AI solution quality, innovation, and operational effectiveness.

Possess 6-8 years of experience in advanced Generative AI and Agentic Framework concepts. Proficiency in Cloud Application Integration and Deployment, FastAPI, and Python is essential.

Strong expertise in AI Search Index, AI Agents & Tool Calling, Retrieval-Augmented Generation (RAG), Agentic AI Systems, LangGraph, LangChain, Vector Databases, and Prompt Engineering is mandatory. Experience with platforms such as Amazon Bedrock, Azure OpenAI Service, Azure AI Foundry, Grog, or AI Search Index is required.

Demonstrate strong ownership and a collaborative approach. Foster innovation and quality-focused engineering through proactive experimentation and continuous improvement. Apply strong analytical thinking to evaluate complex AI, retrieval, and orchestration challenges. Adaptability to evolving AI technologies and business requirements is key. Communicate effectively regarding AI solution design, risks, dependencies, assumptions, and improvement opportunities.

Maintain high attention to detail across AI architecture, prompt design, workflow implementation, testing, and deployment. Encourage continuous improvement in AI engineering practices. Promote secure, scalable, and responsible AI engineering practices while balancing innovation, governance, and business objectives.