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Lead Data Scientist

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Onsite

Lead Data Scientist

Iris Software Inc.

Lead Data Scientist

Skills

PythonNumPypandasData AnalysisAIAzure

1 month ago

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Important information


Contract type:

Permanent

Location:

Noida, Uttar Pradesh, India

Work mode:

Onsite

Published on:

16 September 2026

What they need


Why Join Iris? Are you ready to do the best work of your career at one of India’s Top 25 Best Workplaces in IT industry ? Do you want to grow in an award-winning culture that truly values your talent and ambitions ?

Join Iris Software — one of the fastest-growing IT services companies — where you own and shape your success story .

About Us At Iris Software, our vision is to be our client’s most trusted technology partner, and the first choice for the industry’s top professionals to realize their full potential.

With over 4,300 associates across India, U.S.A, and Canada, we help our enterprise clients thrive with technology-enabled transformation across financial services, healthcare, transportation & logistics, and professional services.

Our work covers complex, mission-critical applications with the latest technologies, such as high-value complex Application & Product Engineering, Data & Analytics, Cloud, DevOps, Data & MLOps, Quality Engineering, and Business Automation.

Working with Us At Iris, every role is more than a job — it’s a launchpad for growth.

Our Employee Value Proposition, “Build Your Future. Own Your Journey.” reflects our belief that people thrive when they have ownership of their career and the right opportunities to shape it.

We foster a culture where your potential is valued, your voice matters, and your work creates real impact. With cutting-edge projects, personalized career development, continuous learning and mentorship, we support you to grow and become your best — both personally and professionally.

Curious what it’s like to work at Iris? Head to this video for an inside look at the people, the passion, and the possibilities. Watch it here.

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

Perks And Benefits For Irisians Iris provides world-class benefits for a personalized employee experience. These benefits are designed to support financial, health and well-being needs of Irisians for a holistic professional and personal growth. Click here to view the benefits.

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