Join Iris Software, a top-tier IT company recognized among India's best workplaces, and contribute to innovative Generative AI solutions. This role is for a seasoned GenAI Architect passionate about designing, building, and leading the implementation of advanced AI capabilities across enterprise use cases.

Leverage your expertise in Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), vector databases, and agentic AI frameworks. You will play a crucial role in shaping scalable, secure, and production-ready GenAI platforms and applications, working collaboratively with business stakeholders and engineering teams.

Architect and design comprehensive GenAI solutions, including RAG pipelines and agentic workflows. Translate complex business challenges into actionable GenAI use cases and detailed implementation roadmaps. Develop and implement sophisticated retrieval systems using embeddings and advanced filtering techniques. Select, integrate, and optimize vector databases for superior indexing and retrieval performance. Construct agentic systems utilizing frameworks like LangChain and LangGraph. Define robust cloud architecture patterns for scalable and secure GenAI deployments. Drive productionization through efficient CI/CD pipelines and containerization best practices. Uphold responsible AI principles, ensuring security, governance, and compliance. Provide technical leadership, mentorship, and guidance to engineering teams. Collaborate with product and delivery teams to align solutions with business objectives and timelines.

Demonstrate strong knowledge of GenAI algorithms and LLM concepts, including prompting, RAG, embeddings, and hallucination control. Possess hands-on experience designing enterprise GenAI use cases such as document Q&A, copilots, and workflow automation. Have a solid understanding of vector databases and similarity search concepts, including indexing and ANN search. Exhibit strong working knowledge of agentic AI frameworks like LangChain and LangGraph. Show experience with cloud services and architecture for GenAI workloads, including compute, networking, and security. Proven experience in implementing CI/CD pipelines for GenAI applications and a deep understanding of deployment patterns including containers and orchestration. Familiarity with DevOps/MLOps practices is essential.