Iris Software
Gen AI Developer
Skills
1 month ago
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Important information
Contract type:
Permanent
Location:
Delhi, India
Work mode:
Hybrid
Published on:
17 September 2026
What they need
Mandatory Skills:
Agentic AI Systems, Advanced GenAI & Agentic Framework Concepts, Cloud Application Integration & Deployment, FastAPI Framework, Python, Graph RAG, Retrieval-Augmented Generation (RAG), AI Agents & Tool Calling, LangChain, LangGraph, Vector Databases, AI Search Index
Key Responsibilities
- Design and develop enterprise Generative AI solutions using Amazon Bedrock, Azure OpenAI Service, Azure AI Foundry, Grog, or AI Search Index platforms.
- Define AI solution architectures and implementation approaches aligned with business and technical objectives.
- Design and implement Retrieval-Augmented Generation (RAG), Graph RAG, and Agentic AI architectures for enterprise use cases.
- Lead 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 and evaluate emerging frameworks such as AutoGen or CrewAI where appropriate.
- Design and optimize prompt strategies, retrieval mechanisms, context orchestration, and response generation frameworks.
- Design prompt engineering pipelines, vector database integration, semantic search solutions, and Retrieval-Augmented Generation architectures supporting enterprise AI applications.
- Design and implement workflows using LangChain or LangGraph to support scalable AI application development.
- Design scalable AI engineering architectures incorporating authentication, authorization, asynchronous processing, scheduling, multithreading, API governance, and enterprise deployment best practices.
- Lead fine-tuning and model customization initiatives to improve domain-specific AI performance.
- Define AI integration patterns and deployment approaches for enterprise application ecosystems.
- Design cloud-native AI integration patterns supporting enterprise APIs, databases, messaging platforms, and event-driven architectures.
- Establish evaluation frameworks for AI response quality, reliability, relevance, and consistency.
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