Inviting Applications For Gen AI Associate Manager at Iris Software

Iris Software; Dadri; On-site; INR 2,500,000 - 4,500,000; Full time

Job summary

Iris Software is seeking a seasoned AI leader to drive adoption and architecture of next-gen AI solutions across the organization. You will lead design and implementation of Generative AI, Agentic AI, Copilot platforms, and API-based LLM integrations.

You will translate business use cases into scalable AI-enabled solutions, define governance frameworks, and mentor teams on best practices for enterprise AI transformation.

Qualifications

• Experience with Generative AI concepts and applications.
• Hands-on with multiple LLMs including OpenAI GPT and Azure OpenAI.
• Understanding model capabilities, tuning, and deployment considerations.

Responsibilities

• Lead design, architecture, and implementation of AI-powered solutions.
• Assess business use cases and translate to scalable AI solutions.
• Architect and develop apps integrating various LLMs via APIs.
• Define AI patterns, governance, and best practices for adoption.
• Design Agentic AI workflows with autonomous decision‑making and multi‑agent collaboration.
• Drive adoption of Microsoft Copilot across teams.
• Develop prompt engineering strategies to improve AI results.
• Evaluate AI models and platforms based on business needs, performance, security, cost.
• Collaborate with stakeholders to identify AI transformation opportunities.
• Stay current with AI technologies and industry trends.
• Mentor teams on AI architecture and implementation best practices.

Skills

Generative AI concepts

LLMs expertise

Agentic AI

Prompt engineering

AI solution architecture

Microsoft Copilot

API integration

Tools

REST APIs

Semantic Kernel

LangChain

LangGraph

Vector Databases

RAG

Job description

Role Overview

We are seeking a highly experienced and hands‑on candidate to drive the adoption, architecture, and implementation of next‑generation AI solutions across the organization. The ideal candidate should possess deep expertise in Generative AI, Agentic AI, Microsoft Copilot, Large Language Models (LLMs), Prompt Engineering, and AI Solution Architecture, along with the ability to guide teams and stakeholders in leveraging emerging AI technologies to solve complex business problems.

Key Responsibilities

• Lead the design, architecture, and implementation of AI-powered solutions leveraging Generative AI, Agentic AI, and Enterprise Copilot platforms.
• Assess business use cases and translate them into scalable AI-enabled solutions.
• Architect and develop applications integrating various LLMs through APIs and AI orchestration frameworks.
• Define AI solution patterns, governance frameworks, and best practices for enterprise adoption.
• Design and implement Agentic AI workflows involving autonomous decision‑making, planning, task execution, and multi‑agent collaboration.
• Drive the adoption and optimization of Microsoft Copilot capabilities across business and engineering teams.
• Develop advanced prompt engineering strategies to improve AI solution accuracy, performance, and business relevance.
• Evaluate and recommend suitable AI models and platforms based on business requirements, performance, security, and cost considerations.
• Collaborate with business stakeholders, product owners, architects, and engineering teams to identify AI transformation opportunities.
• Stay current with emerging AI technologies, industry trends, and evolving capabilities across the Generative AI ecosystem.
• Mentor and guide teams on AI architecture, solution design, and implementation best practices.

Required Skills & Experience

Generative AI & LLM Expertise

• Strong understanding of Generative AI concepts and applications.
• Hands‑on experience with multiple LLMs, including:
• OpenAI GPT Models
• Azure OpenAI
• Other emerging foundation models
• Strong understanding of model capabilities, limitations, fine‑tuning approaches, and deployment considerations.

Agentic AI

• Experience designing and implementing Agentic AI solutions.
• Understanding of:
• AI agents and agent orchestration
• Multi‑agent frameworks
• Autonomous workflow execution
• Tool and function calling
• Planning and reasoning patterns
• Human‑in‑the‑loop architectures

Prompt Engineering

• Expertise in:
• Prompt design patterns
• Chain‑of‑thought prompting
• Few‑shot and zero‑shot techniques
• Prompt optimization and evaluation
• Grounding and context management

AI Integration & Development

• Experience integrating LLMs using APIs.
• Hands‑on knowledge of:
• REST APIs
• Semantic Kernel
• LangChain / LangGraph
• Vector Databases
• RAG (Retrieval‑Augmented Generation)
• Knowledge‑grounded AI solutions