Azure AI Platform- Sr Engineer

Iris Software, Inc.; Dadri, Pune District, Delhi; Hybrid; INR 4,000,000 - 7,000,000; Full time

Job summary

Iris Software, Inc. seeks an experienced GenAI Architect to design and implement enterprise AI solutions using cloud platforms such as Azure OpenAI, Azure Foundry, and Amazon Bedrock.

You will define architectures for RAG, agentic systems, and multi-agent orchestration, and lead development of intelligent AI agents with tool-calling capabilities. The role emphasizes designing scalable AI engineering architectures, prompt strategies, and evaluation frameworks, while mentoring a team on GenAI

Qualifications

• Knowledge of designing enterprise GenAI solutions and AI architectures.
• Experience with RAG, vector stores, and semantic search.
• Familiarity with LangChain, LangGraph or similar orchestration frameworks.

Responsibilities

• Design and develop enterprise Generative AI solutions using cloud platforms.
• Define AI solution architectures aligned with business goals.
• Lead development of intelligent AI agents and tool-calling workflows.
• Design and optimize prompt strategies, retrieval, and context orchestration.
• Mentor team on GenAI frameworks, RAG architectures, and AI engineering best practices.
• Drive continuous improvement in AI solution quality and operational effectiveness.

Skills

RAG

Advanced GenAI

Agentic Framework Concepts

Cloud Integration & Deployment

Agentic AI Systems

Azure AI Foundry

Azure OpenAI Service

AI Agents & Tool Calling

LangChain

Job description

Mandatory Skills: Retrieval-Augmented Generation (RAG), Advanced GenAI & Agentic Framework Concepts, Cloud Application Integration & Deployment, Agentic AI Systems, Azure AI Foundry, Azure OpenAI Service, AI Agents & Tool Calling, LangChain

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.

Behavioral Competencies

• Demonstrates strong ownership while driving AI engineering excellence.
• Collaborate effectively with various teams and business stakeholders to ensure smooth delivery.
• Promotes innovation and quality-focused engineering through proactive experimentation and continuous improvement.