GenAI Solution Architect (Python)

LTM; Pune District, Chennai District, Bengaluru; Remote; INR 4,000,000 - 7,000,000; Full time

Job summary

LTM is seeking an experienced GenAI Solution Architect (Python) to design and build enterprise GenAI applications across PAN India. The role focuses on Python microservices, RAG workflows, and cloud-native architectures.

You will drive best practices for prompt engineering, secure API integrations, and deployment automation, leveraging Azure OpenAI, AWS Bedrock, and other AI platforms. 12+ years of experience required; remote-friendly with national scope.

Qualifications

• 12+ years of experience in GenAI architecture and solution design.
• Strong Python and cloud-native microservices experience.
• Experience with enterprise GenAI platforms and AI deployment.
• Hands-on with API design and security.

Responsibilities

• Design and develop enterprise GenAI applications leveraging RAG and Agentic AI frameworks.
• Architect scalable cloud-native solutions using Python microservices and AI platforms.
• Build secure API integrations, event-driven architectures, and AI-powered workflows.
• Drive GenAI best practices including prompt engineering and deployment automation.

Skills

Python

FastAPI

Flask

REST APIs

Microservices

GenAI

RAG

GraphRAG

Vector Databases

Agentic AI

CrewAI

LangGraph

AutoGen

Azure OpenAI

Azure AI Foundry

AWS Bedrock

Vertex AI

Docker

Kubernetes

CI/CD

Cloud Platforms

Job description

Greetings from LTM

We have Urgent Opening for GenAI Solution Architect (Python)

Experience : 12+ yrs

Location : PAN India

Primary Skills

• Python, FastAPI, Flask, REST APIs, Microservices
• GenAI, RAG, GraphRAG, Vector Databases, Agentic AI
• CrewAI / LangGraph / AutoGen
• Azure OpenAI, Azure AI Foundry, AWS Bedrock, Vertex AI
• Docker, Kubernetes, CI/CD, Cloud Platforms (AWS/Azure/GCP)

Key Responsibilities

• Design and develop enterprise GenAI applications leveraging RAG and Agentic AI frameworks.
• Architect scalable cloud-native solutions using Python microservices and AI platforms.
• Build secure API integrations, event-driven architectures, and AI-powered workflows.
• Drive GenAI best practices including prompt engineering, responsible AI, and deployment automation.