AI Full stack Engineer

LTM; Hyderabad, Bengaluru; Híbrido; INR 1.500.000 - 2.300.000; Jornada completa

Descripción de la vacante

LTM is seeking an AI Full Stack Engineer to design and implement scalable frontend and backend solutions that deliver robust AI-enabled capabilities. You will collaborate with cross-functional teams to translate complex requirements into high-quality software with a focus on performance and reliability.

The role emphasizes full-stack development using Angular, Java, Python, and microservices, along with secure coding practices, containerization, and modern DevOps workflows to accelerate delivery

Formación

• Mandatory skills: Angular, Java, JavaScript, Microservices, Python.

Conocimientos

Angular

Java

JavaScript

Microservices

Python

Descripción del empleo

Note : need only talents who are serving NP / immediate joiners

AI Full Stack Engineer

Mandatory Skills : Angular, Java, JavaScript, Microservices, Python

Good to Have Skills : AI-assisted development & coding, Application Security (application security framework/ threat modelling/ Secure SDLC/ DevSecOps/Application Security Architecture Review), Architecture Patterns and Styles, Claude Code, Cloud AI Design Assistance, Design Patterns, SDLC, Secure design & app security, Software Engineering and Design Architecture, Spring Boot, TypeScript

• AISDLC frameworks like BMAD GSD SPECKIT etc
• Backend Java Python JS clean API design versioning auth error handling observability
• Front end Angular 16 preferred or equivalent modern framework responsive high performance UIs
• Databases SQL and NoSQL schema design indexing query optimization
• APIs RESTful design patterns experience with WebSockets or real time communication preferred
• Infrastructure Docker Kubernetes deployment scaling container orchestration
• Messaging Kafka RabbitMQ or similar asynchronous processing systems
• Proficient in using Claude AI Anthropic Claude Sonnet Haiku for day today engineering tasks
• Prompt engineering ability to craft iterate and optimize prompts for code generation explanation review and test
• creation
• AI integration experience calling LLM APIs Claude Open AI Azure Open AI AWS Bedrock from application code
• Understanding of LLM concepts context windows token limits embeddings RAG basics latency and cost tradeoffs
• Use of AI tools Claude GitHub Copilot or equivalent to measurably reduce development cycle time
• Collaborative development with data scientists ML engineers to productionize AI components