Open to collaborations

DhiranReddy

Associate Backend & AI Engineer @ Unisys

Backend & AI engineer specializing in NestJS & Python.

Building scalable microservices and agentic platforms.

Retro Macintosh showing code

Based in Bengaluru, designing secure, low-latency APIs.

Published researcher in Quantum Neural Networks.

Selected
2023–2025

Works

Voyanta dashboard mockup
01 //
NestJS LLMs Microservices

Voyanta

An intelligent, microservices-driven AI travel planner. Leverages Large Language Models to curate complex, dynamic itineraries tailored to user preferences, with real-time optimization.

RAG Pipeline interface
02 //
Python LangChain Vector DB

RAG Pipeline

Advanced Document QA system using Retrieval-Augmented Generation. Combines Python, LangChain, and high-performance vector databases to parse and query large PDF repositories securely with low latency.

Hand gesture CV system
03 //
Python Computer Vision OpenCV

Hand Gesture Control

Real-time computer vision platform powered by OpenCV and MediaPipe. Translates physical hand gestures into system-level operations, providing hands-free inputs and desktop control.

ResearchGate / Springer Nature 2024

Quantum Neural Networks for Classification Tasks

Published research exploring hybrid classical-quantum models for real-world classification. Covers qubit encoding strategies, variational circuits, and benchmarks against classical baselines.

Read Paper ↗
IEEE / Springer Nature 2024

Aspect-Based Sentiment Analysis using Transformers

NLP research on fine-grained sentiment detection at the aspect level. Implements BERT-based architectures with custom attention mechanisms to extract opinion targets from unstructured text.

Read Paper ↗
Dev.to / Medium Coming Soon

Building Production-Grade RAG Pipelines with LangChain

A practical deep-dive into retrieval-augmented generation for enterprise document QA — covering chunking strategies, vector store selection, query routing, and latency optimization.

Follow for updates ↗
Dev.to / Medium Coming Soon

Rate-Limiting & Auth Middleware Patterns in NestJS

Enterprise patterns for securing microservices — JWT guard composition, Redis-backed throttling, and Salesforce OAuth integration. Based on production experience at Unisys.

Follow for updates ↗
N

Jetson AI Specialist

NVIDIA

2023
G

Introduction to Generative AI

Google Cloud

2024
DL

Generative AI with LLMs

DeepLearning.AI / Coursera

2024
U

AI Foundation Program

Unisys Global

2023
Py

Python for Data Science & AI

IBM / Coursera

2022
N

NestJS — Zero to Hero

Udemy

2023
Professional

Timeline

Unisys & Research

1.5+ years building scalable REST APIs, microservices, and AI-driven platforms, supported by published academic research.

Associate Software Engineer – Backend & AI

Aug 2025 – Present
Unisys • Bengaluru, KA
  • Building an agentic AI platform for automated code generation, integrating LLM workflows directly into the dev lifecycle.
  • Developed a secure NestJS AI API middleware featuring user session management, rate-limiting, and consumption tracking.
  • Delivered core Salesforce integrations including Paymentus (payment gateway), Google Analytics, and automated document archival workflows.
  • Engineered a Salesforce log analytics dashboard parsing raw system logs to render real-time telemetry in a custom interface.

CA&I Intern – Backend Developer

Oct 2024 – Aug 2025
Unisys • Bengaluru, KA
  • Maintained a high-traffic reusable component platform exposing 150+ microservice REST APIs in NestJS, Node.js, and Python.
  • Resolved critical production database bottleneck timeouts, substantially increasing server throughput and stability.
  • Authored comprehensive Swagger/OpenAPI specifications, set up Docker environments, and configured secure Bearer Token/Auth Guards.

Academic Publications

Towards a Nuanced Sentiment Analysis: A Novel Hybrid Approach to Aspect-Based Insights
Springer Nature / IEEE

Introduced a hybrid BERT + LSTM deep learning model for sentence aspect classification and granular opinion mining in natural language documents.

Enhancing Neural Networks with Quantum Computing
Springer Nature / IEEE

Investigated hybrid Quantum Neural Networks (QNN) architectures that run quantum layers alongside deep learning parameters to optimize processing performance.

Technical

Capabilities

01 /
Languages
Python TypeScript JavaScript SQL C / C++
02 /
Frameworks & AI
NestJS Node.js FastAPI Django LangChain OpenAI SDK RAG Scikit-Learn TensorFlow
03 /
DB & Cloud
PostgreSQL MongoDB Azure Blob Storage Azure VM / AD Vector Databases
04 /
Tools & Systems
Docker Git Swagger / OpenAPI Salesforce Integration Power BI & Tableau Odoo ERP
Personal

Story

"Bridging the cooperative leadership of a cricket pitch with complex architectural algorithms."

I graduated with Honors in Artificial Intelligence & Machine Learning from New Horizon College of Engineering, achieving a CGPA of 9.62/10 (Department Rank 2). During my studies, I was chosen as an NVIDIA Jetson Student Ambassador, driving Edge AI, CUDA, and hardware workshops for over 120+ student peers.

Outside of development sprints and deep-learning training jobs, I have a deep passion for sports. I represented Karnataka's KYCA cricket league as the Captain of the u16 Cricket Team. I bring that same level of strategic thinking, execution, and collaborative team captaincy into building microservices and working in tech teams.

Ready to build or collaborate? dhirankumarreddy2003@gmail.com