Lead Data Scientist

Iris Software, Inc.; Dadri; On-site; INR 1,800,000 - 3,000,000; Full time

Job summary

Iris Software in Noida invites a Lead Data Scientist to shape enterprise GenAI strategy across business units. You will define and govern AI engineering standards, drive scalable AI architectures, and guide multi-agent implementations with HITL governance.

Responsibilities include establishing RAG standards, tool-calling frameworks, and secure deployment patterns using LangChain, Azure OpenAI, and cloud integration. Leadership and collaboration with stakeholders are essential.

Qualifications

• Advanced GenAI concepts and architectures.
• Hands-on with LangChain / LangGraph in production.
• Experience with RAG and tool-calling for enterprise AI.

Responsibilities

• Define and drive enterprise Generative AI strategy aligned with organizational objectives, innovation goals, and AI transformation initiatives.
• Establish AI engineering standards, governance frameworks, and best practices for enterprise AI solution delivery.
• Lead the design of enterprise-scale AI architecture using Bedrock, Azure OpenAI Service, Azure AI Foundry, Grog, or AI Search Index platforms.
• Define enterprise standards for RAG, Graph RAG, Agentic AI systems, and intelligent automation architectures.
• Drive adoption of enterprise multi-agent architectures, HITL governance, and scalable AI engineering patterns.

Skills

GenAI Concepts

LangChain

LangGraph

RAG

Azure OpenAI

AI Agents Tools

Tool Calling

Python

Pandas

NumPy

Cloud App Integration

Vector Databases

Model Fine-Tuning

MCP

Tools

Azure OpenAI Service

Job description

Lead Data Scientist

Location: Noida

Company: Iris Software

Job Description Mandatory Skills

• Advanced GenAI Agentic Framework Concepts
• Cloud Application Integration Deployment
• Agentic AI Systems
• Retrieval-Augmented Generation (RAG)
• Azure OpenAI Service
• AI Agents Tool Calling
• LangChain

Key Responsibilities

• Define and drive enterprise Generative AI strategy aligned with organizational objectives, innovation goals, and AI transformation initiatives.
• Establish AI engineering standards, governance frameworks, and best practices for enterprise AI solution delivery.
• Lead the design of enterprise-scale AI architecture using Amazon Bedrock, Azure OpenAI Service, Azure AI Foundry, Grog, or AI Search Index platforms.
• Define enterprise standards for Retrieval-Augmented Generation (RAG), Graph RAG, Agentic AI systems, and intelligent automation architectures. Drive adoption of AI agents, tool-calling frameworks, and autonomous workflow solutions across business functions.
• Drive adoption of enterprise multi-agent architectures, Human-in-the-Loop (HITL) governance, and scalable AI engineering patterns supporting secure and reliable business automation.
• Establish governance standards for model customization, fine-tuning, prompt engineering, retrieval quality, and AI solution lifecycle management.
• Define enterprise standards for prompt engineering pipelines, vector databases, semantic search, Retrieval-Augmented Generation, Model Context Protocol (MCP), and AI agent orchestration frameworks.
• Define architecture patterns and engineering standards using LangChain, LangGraph, and related workflow orchestration frameworks.
• Establish enterprise AI engineering standards covering secure API design, authentication, authorization, asynchronous processing, scheduling, scalability, deployment, observability, and operational resilience.
• Establish cloud integration and deployment standards for scalable and secure AI-enabled applications.
• Establish enterprise integration standards supporting databases, enterprise APIs, messaging platforms, and cloud-native AI application deployment.
• Lead architecture reviews and ensure AI solutions meet scalability, reliability, maintainability, explainability, and business value objectives.
• Guide teams on GenAI architecture, agentic systems, AI governance, and enterprise AI adoption best practices.
• Identify AI-related risks, governance gaps, operational challenges, and architectural limitations while defining mitigation strategies.
• Collaborate with various teams and leadership stakeholders to align AI initiatives with organizational objectives.
• Drive continuous improvement initiatives focused on AI maturity, innovation, operational effectiveness, governance, and business value realization.

Mandatory Competencies

• Data AI - GEN AI - Prompt Engineering / Vector Databases
• Data AI - GEN AI - Workflow Agentic Frameworks (LangChain / LangGraph)
• Data AI - GEN AI - Fine tuning Model Customization / AI Agents Tool Calling
• Data AI - GEN AI - Retrieval Augmented Generation (RAG) / Graph RAG / Agentic AI Systems
• Data AI - GEN AI - Azure OpenAI Service
• Data AI - GEN AI - Pandas
• Data AI - GEN AI - NumPy
• Data AI - GEN AI - Cloud Application Integration Deployment
• Data AI - GEN AI - Advanced GenAI Agentic Framework Concepts
• Data AI - GEN AI - Python
• Beh - Communication and collaboration