Advance your career at Iris Software, a recognized Top 25 Best Workplace in India's IT industry. We are seeking a Senior Engineer to join our dynamic team and contribute to innovative Generative AI solutions.

Iris Software is a rapidly expanding IT services firm dedicated to being a trusted technology partner. With a global presence and a focus on cutting-edge technologies like AI, Data & Analytics, and Cloud, we empower enterprise clients across various sectors to achieve technological transformation.

At Iris, we believe in empowering our employees. Our culture fosters growth, values your contributions, and provides opportunities for ownership of your career journey. Benefit from advanced projects, continuous learning, and mentorship to achieve your professional and personal goals.

Design and implement advanced enterprise Generative AI solutions utilizing platforms such as Amazon Bedrock and Azure OpenAI Service.

Develop AI solution architectures and define implementation strategies aligned with business and technical goals.

Create and deploy Retrieval-Augmented Generation (RAG), Graph RAG, and Agentic AI architectures tailored for enterprise needs.

Lead the development of intelligent AI agents, tool-calling functionalities, and frameworks for autonomous task execution.

Orchestrate multi-agent systems using frameworks like LangGraph and explore emerging tools such as AutoGen or CrewAI.

Optimize prompt strategies, retrieval mechanisms, and response generation frameworks for maximum efficiency.

Develop prompt engineering pipelines, integrate vector databases, and build semantic search capabilities.

Implement workflows using LangChain or LangGraph to support scalable AI application development.

Design and implement scalable AI engineering architectures, ensuring security, performance, and adherence to enterprise deployment best practices.

Lead fine-tuning and model customization efforts to enhance domain-specific AI performance.

Define AI integration patterns and deployment strategies within complex enterprise application ecosystems.

Establish robust evaluation frameworks to assess AI response quality, reliability, and relevance.

Design Human-in-the-Loop (HITL) workflows to continuously improve AI quality and business reliability.

Review AI solution designs, ensuring alignment with engineering standards, scalability, and responsible AI principles.

Troubleshoot complex AI workflow, retrieval, and model behavior issues through detailed root cause analysis.

Mentor team members on GenAI frameworks, RAG architectures, agentic systems, and AI engineering best practices.

Requires 6-8 years of experience in Generative AI and advanced agentic framework concepts.

Proficiency in Cloud Application Integration and Deployment, FastAPI Framework, and Python is essential.

Experience with AI Search Index, AI Agents, Tool Calling, Retrieval-Augmented Generation (RAG), and Agentic AI Systems is mandatory.

Must have hands-on experience with LangGraph, LangChain, Vector Databases, and Prompt Engineering.

Demonstrated ability to design and develop enterprise Generative AI solutions using platforms like Amazon Bedrock, Azure OpenAI Service, Azure AI Foundry, Grog, or AI Search Index.

Strong understanding of AI solution architectures, implementation approaches, and Retrieval-Augmented Generation (RAG), Graph RAG, and Agentic AI architectures.

Proven experience in leading the development of intelligent AI agents, tool-calling workflows, and autonomous task execution frameworks.

Experience designing and implementing multi-agent orchestration solutions using frameworks such as LangGraph.

Skilled in designing and optimizing prompt strategies, retrieval mechanisms, context orchestration, and response generation frameworks.

Expertise in designing prompt engineering pipelines, vector database integration, semantic search solutions, and RAG architectures.

Experience designing and implementing workflows using LangChain or LangGraph for scalable AI application development.

Ability to design scalable AI engineering architectures incorporating authentication, authorization, asynchronous processing, scheduling, multithreading, API governance, and enterprise deployment best practices.

Experience leading fine-tuning and model customization initiatives to improve domain-specific AI performance.

Proficiency in defining AI integration patterns and deployment approaches for enterprise application ecosystems.

Experience designing cloud-native AI integration patterns supporting enterprise APIs, databases, messaging platforms, and event-driven architectures.

Ability to establish evaluation frameworks for AI response quality, reliability, relevance, and consistency.

Skilled in designing Human-in-the-Loop (HITL) workflows and evaluation mechanisms.

Experience reviewing AI solution designs to ensure adherence to engineering standards, scalability, maintainability, and responsible AI practices.

Strong analytical and troubleshooting skills for complex AI workflow, retrieval, orchestration, and model behavior challenges.

Ability to mentor team members on GenAI frameworks, RAG architectures, agentic systems, and AI engineering best practices.

Excellent communication and collaboration skills, with a proactive approach to innovation and quality-focused engineering.