Job Description

Experience: 6-8 Years

Location: Noida

Mandatory Skills

- Advanced GenAI Agentic Framework Concepts
- LangGraph
- LangChain
- Vector Databases
- Prompt Engineering
- Retrieval-Augmented Generation (RAG)
- Agentic AI Systems
- FastAPI Framework
- Python

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.
- Applies robust analytical thinking to evaluate complex AI, retrieval, and orchestration challenges.
- Demonstrates adaptability while managing evolving AI technologies, frameworks, and business requirements.
- Communicates effectively regarding AI solution design, risks, dependencies, assumptions, and improvement opportunities.
- Maintains high attention to detail across AI architecture, prompt design, workflow implementation, testing, and deployment activities.
- Encourages continuous improvement in AI engineering practices, framework adoption, and solution effectiveness.
- Promotes secure, scalable, and responsible AI engineering practices while balancing innovation, governance, and business objectives.

Company

Company: Iris Software

📌 Gen AI - Senior Engineer (Noida)
🏢 IRIS SOFTWARE
📍 Noida