Join Iris Software, a rapidly expanding IT services firm recognized among India's Top 25 Best Workplaces in IT. We are dedicated to being our clients' most trusted technology partner and a premier destination for top industry talent. With a global team across India, the U.S.A., and Canada, we empower enterprise clients in financial services, healthcare, transportation & logistics, and professional services through technology transformation.

Our expertise spans complex, mission-critical applications leveraging cutting-edge technologies including Application & Product Engineering, Data & Analytics, Cloud, DevOps, Data & MLOps, Quality Engineering, and Business Automation. At Iris, your career is a journey of ownership and growth, supported by a culture that values your potential, amplifies your voice, and ensures your work creates significant impact. We offer personalized career development, continuous learning, and mentorship to help you excel both personally and professionally.

Design and develop advanced Generative AI solutions utilizing platforms like Amazon Bedrock, Azure OpenAI Service, Azure AI Foundry, Grog, or AI Search Index. Define robust AI solution architectures and implementation strategies that align with both business and technical goals. Implement sophisticated RAG, Graph RAG, and Agentic AI architectures for enterprise use cases, alongside developing intelligent AI agents, tool-calling workflows, and autonomous task execution frameworks.

Lead the design and implementation of multi-agent orchestration solutions using frameworks such as LangGraph, and evaluate emerging frameworks like AutoGen or CrewAI. Optimize prompt strategies, retrieval mechanisms, context orchestration, and response generation frameworks. Design prompt engineering pipelines, vector database integrations, semantic search solutions, and RAG architectures to support enterprise AI applications.

Develop scalable AI engineering architectures incorporating essential elements like authentication, authorization, asynchronous processing, scheduling, multithreading, API governance, and enterprise deployment best practices. Lead fine-tuning and model customization initiatives to enhance domain-specific AI performance. Define AI integration patterns and deployment approaches for enterprise application ecosystems, ensuring cloud-native design.

Establish comprehensive evaluation frameworks for AI response quality, reliability, relevance, and consistency. Design Human-in-the-Loop (HITL) workflows and evaluation mechanisms to improve AI quality, governance, and business reliability. Conduct thorough reviews of AI solution designs to ensure adherence to engineering standards, scalability, maintainability, and responsible AI practices. Troubleshoot complex AI workflow, retrieval, orchestration, and model behavior challenges through detailed root cause analysis. Mentor team members on GenAI frameworks, RAG architectures, agentic systems, and AI engineering best practices. Drive continuous improvement initiatives focused on AI solution quality, innovation, and operational effectiveness.

Possess 6-8 years of experience in Generative AI and Agentic Framework Concepts, Cloud Application Integration & Deployment, Python, Azure OpenAI Service, Agentic AI Systems, AI Agents & Tool Calling, and LangChain. Demonstrate strong ownership while driving AI engineering excellence and collaborate effectively with various teams and business stakeholders for smooth delivery. Promote innovation and quality-focused engineering through proactive experimentation and continuous improvement.

Apply strong analytical thinking to evaluate complex AI, retrieval, and orchestration challenges. Exhibit adaptability while managing evolving AI technologies, frameworks, and business requirements. 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 activities. Encourage continuous improvement in AI engineering practices, framework adoption, and solution effectiveness. Promote secure, scalable, and responsible AI engineering practices while balancing innovation, governance, and business objectives.