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Gen AI Developer

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Hybrid

Gen AI Developer

Iris Software

Gen AI Developer

Skills

PythonAIFastAPIAzure

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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