Artificial Intelligence Engineer

Infosys; Hyderabad, Chennai District, Bengaluru; On-site; INR 1,500,000 - 3,000,000; Full time

Job summary

Infosys is seeking an AI Solution Architect in Hyderabad to design end-to-end AI/GenAI architectures and develop scalable AI platforms with LLMs and agentic frameworks.

You will produce HLD/LLD documents, define governance and responsible AI practices, and build AI-powered solutions including chatbots, memory, and automation components. Strong Python/SQL skills and cloud/MLOps experience are required.

Qualifications

• Design end-to-end AI architectures and solutions.
• more descriptions are provided in the HTML.

Responsibilities

• Design end-to-end AI/GenAI architectures and technical solutions.
• Build scalable AI platforms leveraging LLMs and Agentic AI frameworks.
• Create HLD/LLD documents for AI apps.
• Define AI governance, security, and responsible AI practices.
• Develop and deploy LLM-based applications and RAG solutions.
• Fine-tune and evaluate foundation models for businessUse cases.
• Build AI-powered chatbots, assistants, and knowledge management.
• Develop prompt engineering strategies and evaluation frameworks.
• Design autonomous AI agents and multi-agent workflows.
• Integrate agents with enterprise apps, APIs, and data sources.
• Develop APIs and microservices for AI deployment.
• Implement MLOps pipelines, optimize performance and cost.

Skills

Python

SQL

LLMs

Prompt Engineering

RAG

Fine-Tuning

AI Agents

Multi-Agent Systems

Tool Calling

Agent Orchestration

LangChain

LangGraph

LlamaIndex

CrewAI

AutoGen

OpenAI

Claude

Gemini

Open Source LLMs

NLP

Deep Learning

Model Evaluation

AWS

Azure

GCP AI Services

Docker

Kubernetes

CI/CD

Model Deployment

Vector Databases

Pinecone

Chroma

Weaviate

FAISS

SQL/NoSQL

REST APIs

Microservices

Tools

Python

Job description

Role & responsibilities

AI Solution Design & Architecture

• Design end-to-end AI/GenAI architectures and technical solutions.
• Build scalable and secure AI platforms leveraging LLMs and Agentic AI frameworks.
• Create High-Level Design (HLD) and Low-Level Design (LLD) documents for AI applications.
• Define AI governance, security, and responsible AI best practices.

Generative AI Development

• Develop and deploy applications using Large Language Models (LLMs).
• Design and implement Retrieval-Augmented Generation (RAG) solutions.
• Fine-tune, optimize, and evaluate foundation models for business use cases.
• Build AI-powered chatbots, virtual assistants, knowledge management, and automation solutions.
• Develop prompt engineering strategies and model evaluation frameworks.

Agentic AI & Multi-Agent Systems

• Design and implement autonomous AI agents and multi-agent workflows.
• Build agent orchestration, tool integration, memory management, and reasoning capabilities.
• Develop intelligent systems capable of planning, decision-making, and task automation.
• Integrate AI agents with enterprise applications, APIs, and data sources.

Development & Integration

• Develop AI applications using Python and modern AI frameworks.
• Build APIs and microservices for AI model deployment and integration.
• Implement scalable AI pipelines and production-grade MLOps processes.
• Optimize model performance, latency, and cost efficiency.

Required Skills

• Programming: Python, SQL
• Generative AI: LLMs, Prompt Engineering, RAG, Fine-Tuning
• Agentic AI: AI Agents, Multi-Agent Systems, Tool Calling, Agent Orchestration
• Frameworks: LangChain, LangGraph, LlamaIndex, CrewAI, AutoGen
• AI Models: OpenAI, Claude, Gemini, Open Source LLMs
• Machine Learning: NLP, Deep Learning, Model Evaluation
• Cloud: AWS, Azure, or GCP AI Services
• MLOps: Docker, Kubernetes, CI/CD, Model Deployment
• Databases: Vector Databases (Pinecone, Chroma, Weaviate, FAISS), SQL/NoSQL
• APIs & Integration: REST APIs, Microservices