Headline

AI Engineer (Backend) Intern @L&T | Agentic AI & Distributed Systems | Java & Python | System Design | 430+ @LeetCode | Spring Boot & FastAPI | Microservices | Kafka & Redis | Docker | Hybrid RAG | HLD | LLD |

About

Building reliable software has always interested me more than simply writing code. I enjoy understanding complex problems, designing scalable solutions and turning ideas into production ready systems that solve real business challenges. Currently, I am working as an AI Engineer Intern at LTTS, where I build intelligent applications powered by LLMs, Retrieval Augmented Generation (RAG), AI agents, LangChain, LangGraph, and modern GenAI workflows. Alongside AI, I have a strong backend engineering foundation in Java, Python, Spring Boot, REST APIs, microservices, authentication, caching and Architecture design. I enjoy working across the entire lifecycle of an application from designing system architecture and developing backend services to integrating AI capabilities into enterprise grade products. My interests lie in building scalable backend systems, intelligent automation, enterprise AI solutions and production ready AI applications that deliver measurable business value. I believe great engineering is not just about using the latest technologies it's about building systems that are reliable, maintainable, secure and scalable. I continuously invest time in learning distributed systems, software architecture, cloud technologies and modern AI engineering to become a better problem solver every day. I'm always excited to connect with engineers, researchers, and professionals who are passionate about Backend Engineering, Generative AI, AI Agents, LLMs, and scalable software systems.

Location

Bengaluru

AI stack

claude code

claude

gemini

github copilot

openai

railway

Work experience

AI Engineer Intern at Akshpro, May 2026 - Present · 4 mos, Bengaluru, Karnataka, India, 🏢 Client: L&T Technology Services (LTTS) 📍 Client Location: Bengaluru, Karnataka, India ◆ AI Validation & Agentic Systems •Delivered an enterprise-grade AI Brand Validation solution that automated the evaluation of 200+ brand-compliance rules and reduced an estimated 4 to 8 hours of manual review per asset to minutes through a two-stage multimodal AI workflow. The solution transformed complex brand guidelines into evidence-backed violations and actionable corrections across logos, colours, typography, layout and messaging, enabling faster, more consistent reviews while significantly reducing manual effort and rework. •Improved enterprise Super Agent routing accuracy from 60% to 85% by resolving intent-classification, incorrect tool-selection and fallback failures. Implemented configuration-driven routing enhancements that increased successful tool execution by 90%, reduced incorrect responses and delivered a more reliable user experience with minimal architectural disruption.

Education

Bachelor of Engineering - BE, Computer Science, Atria Institute of technology

Public links

github: https://github.com/Shrinath-816