Gen AI - Senior Engineer

Iris Software; Dadri, Greater Noida, Pune District; On-site; INR 1,800,000 - 3,000,000; Full time

Job summary

Iris Software, Inc. seeks a senior GenAI architect to design enterprise AI solutions and scalable agentic systems. You will craft architectures for RAG, tool-calling workflows, and autonomous agents within cloud-native deployments.

You will lead the evolution of prompt engineering, vector databases, and semantic search to support enterprise AI applications, collaborating across teams.

Qualifications

• Experience designing enterprise GenAI solutions and architectures.
• Familiarity with LangChain/LangGraph, RAG, and agentic AI patterns.
• Ability to define AI integration patterns and deployment approaches.

Responsibilities

• Design enterprise Generative AI solutions using Bedrock, Azure OpenAI, and related platforms.
• Define AI solution architectures aligned with business goals and technical constraints.
• Design and implement Retrieval-Augmented Generation (RAG) architectures for enterprise use cases.
• Lead development of intelligent AI agents, tool-calling workflows, and autonomous task execution frameworks.
• Design multi-agent orchestration using LangGraph; evaluate AutoGen or CrewAI as applicable.
• Develop scalable prompt strategies, vector DB integration, and semantic search for enterprise apps.

Skills

Python

Advanced GenAI

Agentic Framework

Cloud Integration

Azure OpenAI Service

RAG

LangGraph

LangChain

Prompt Engineering

Tools

FastAPI

Azure OpenAI Service

LangGraph

LangChain

Grog

AutoGen

CrewAI

Job description

Role & responsibilities

Python, Advanced GenAI & Agentic Framework Concepts, Cloud Application Integration & Deployment, FastAPI Framework, Azure OpenAI Service, Retrieval-Augmented Generation (RAG), AI Agents & Tool Calling, LangGraph, LangChain, Prompt Engineering

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.
• Design Human-in-the-Loop (HITL) workflows and evaluation mechanisms to improve AI quality, governance, and business reliability.
• Review AI solution designs to ensure adherence to engineering standards, scalability, maintainability, and responsible AI practices.
• Troubleshoot complex AI workflow, retrieval, orchestration, and model behavior challenges through detailed root cause analysis.
• Mentor team members on GenAI frameworks, RAG architectures, agentic systems, and AI engineering best practices.
• Drive continuous improvement initiatives focused on AI solution quality, innovation, and operational effectiveness.