Fullstack Gen Ai Gcp - Lead

Iris Software; Dadri; On-site; INR 300,000 - 600,000; Full time

Benefits offered by this job

World-class benefits

Job summary

Iris Software is seeking a senior AI architect to lead an enterprise GenAI strategy and define governance for scalable AI solutions in India. The role focuses on architecting AI systems, enabling tool-calling capabilities, and ensuring alignment with business objectives.

The successful candidate will establish standards for RAG, multi-agent architectures, and secure, observable deployments while collaborating with cross-functional teams to realize AI-driven value.

Qualifications

• Define and drive enterprise Generative AI strategy aligned with objectives and AI transformation initiatives.
• Establish AI engineering standards, governance frameworks, and best practices for enterprise AI delivery.
• Lead the design of enterprise-scale AI architecture using Bedrock, Azure OpenAI, Azure Foundry, AI Search Index platforms.
• Define enterprise standards for RAG, Graph RAG, Agentic AI systems, and intelligent automation architectures.
• Drive adoption of AI agents, tool-calling frameworks, and autonomous workflow solutions.
• Drive adoption of multi-agent architectures, HITL governance, and scalable AI engineering patterns.
• Establish governance standards for model customization, fine-tuning, prompt engineering, and lifecycle management.
• Define enterprise standards for prompt pipelines, vector databases, semantic search, MCP, and orchestration frameworks.
• Define architecture patterns and engineering standards using LangChain, LangGraph, and related frameworks.
• Establish enterprise AI engineering standards covering secure API design, authentication, asynchronous processing, scheduling, scalability, deployment, observability, and resilience.
• Establish cloud integration and deployment standards for scalable AI-enabled apps.
• Establish enterprise integration standards for databases, APIs, messaging, and cloud-native deployment.
• Lead architecture reviews ensuring scalability, reliability, and business value.
• Guide GenAI architecture, agentic systems, governance, and enterprise adoption best practices.
• Identify AI risks, governance gaps, and mitigation strategies.
• Collaborate with teams to align AI initiatives with business objectives.
• Drive continuous improvement in AI maturity, innovation, governance, and value realization.

Responsibilities

• Define and drive enterprise Generative AI strategy aligned with objectives 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, AI Search Index platforms.
• Define enterprise standards for 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, 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, 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.

Skills

Agentic AI Systems

Advanced GenAI Agentic Framework

Cloud Application Integration

LangChain

LangGraph

AI Search Index

AI Agents Tool Calling

Python

Terraform

Tools

Python

Terraform

Job description

Mandatory Skills:

Agentic AI Systems, Advanced GenAI Agentic Framework Concepts, Cloud Application Integration Deployment, LangChain, LangGraph, AI Search Index, AI Agents Tool Calling

Additional Skills:

Python, Terraform

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.

Behavioral Competencies

• Demonstrates leadership and accountability in driving AI engineering excellence across programs and initiatives.
• Collaborate effectively with various teams and business stakeholders to ensure smooth delivery.
• Promotes a culture of innovation, responsible AI adoption, quality, and continuous improvement.
• Applies strategic thinking to address AI risks, architectural challenges, governance requirements, and business priorities.
• Demonstrates strong decision-making while balancing innovation, scalability, reliability, governance, and business objectives.
• Communicates effectively regarding AI strategy, risks, dependencies, solution outcomes, and business value.

Mandatory Competencies

• DevOps/Configuration Mgmt - DevOps/Configuration Mgmt - Terraform
• Data AI - GEN AI - Python
• Cloud - GCP - Apigee API Management, API Gateway
• Data AI - GEN AI - Cloud Application Integration Deployment
• 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 - AI Search Index
• Data AI - GEN AI - Pandas
• Data AI - GEN AI - NumPy
• Data AI - GEN AI - Advanced GenAI Agentic Framework Concepts
• 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 to view the benefits.