Senior Technical Architect

About the role

Iris Software is seeking a Senior Technical Architect (GenAI Architect) to design, build, and guide the implementation of Generative AI solutions across enterprise use cases. Day-to-day work involves architecting end-to-end GenAI solutions including RAG pipelines, agentic workflows, and multimodal use cases; designing retrieval systems and vector database integrations; building agentic systems with LangChain/LangGraph; defining cloud architecture patterns; driving CI/CD productionization; and providing technical leadership and mentoring to engineering teams. The role is based in Noida, UP, India.

What you’ll do

• Architect and design end-to-end GenAI solutions including RAG pipelines, agentic workflows, and multimodal use cases
• Translate business problems into GenAI-driven use cases, solution blueprints, and implementation roadmaps
• Design and implement retrieval systems using embeddings, chunking strategies, metadata filters, reranking, and evaluation metrics
• Select and integrate vector databases and optimize indexing, retrieval performance, and relevance tuning
• Build agentic systems using frameworks such as LangChain, LangGraph, and related orchestration tools
• Define cloud architecture patterns for scalable, secure, and reliable GenAI deployments
• Drive productionization using CI/CD pipelines, containerization using best practices
• Ensure responsible AI practices including security, governance, privacy, compliance, and monitoring

What you’ll bring

• Strong hands-on and architectural expertise in LLMs, RAG, vector databases, and agentic AI frameworks
• Experience in cloud-native deployments and CI/CD automation
• Strong knowledge of GenAI algorithms and LLM concepts: prompting, fine-tuning vs RAG, embeddings, context windows, token limits, hallucination control
• Experience designing enterprise GenAI use cases (document Q&A, copilots, summarization, search, workflow automation, customer support, knowledge assistants)
• Understanding of evaluation techniques: groundedness, relevance, faithfulness, latency/cost trade-offs
• Hands-on understanding of vector databases and similarity search concepts: embeddings, indexing, ANN search, hybrid search, metadata filtering
• Experience with vector database tools like Pinecone, FAISS, Weaviate, Chroma, Milvus, Azure AI Search / Elastic (vector)
• Strong working knowledge of agentic frameworks such as LangChain, LangGraph, MCP, tool calling/function calling, memory, planning, multi-agent workflows, guardrails

Skills