Headline

Software Engineer | Java, Kotlin, Spring Boot, Azure, Kubernetes, Kafka | GenAI: RAG, LangChain, AWS Bedrock | Amazon AIdeaS 2026 Winner (RetainIQ) | PGDM Finance @ XLRI

About

I build cloud-native backend systems for wealth management: a data mesh on Azure, Java and Kotlin microservices for post-trade data and reporting, and Terraform and Ansible automation across 100+ cloud resources that cut environment setup time by 80%. GitLab CI/CD pipelines I built halved manual release effort. On the AI side, I won the Innovation category at Amazon AIdeaS 2026 with RetainIQ (retainiq.cloud), a knowledge-risk platform built serverless on AWS Bedrock. It detects bus-factor risk in teams and scores every critical topic 0 to 100 before the knowledge walks out the door. Day to day, I lead my team's shift to AI-assisted, spec-driven development. Toolbox: Java, Kotlin, Python, Spring Boot, Kafka, Kubernetes, Docker, Terraform, Azure, AWS, LangChain, RAG, Azure OpenAI. Beyond code: pursuing a PGDM in Finance at XLRI Jamshedpur, published ML research in Procedia Computer Science (Elsevier), Gold Certified Engineer and Differentiator Award at my current firm, and I write weekly at blog.samyak.space. Portfolio: samyak.space

Location

Pune District

AI stack

antigravity

opencode

Work experience

Software Engineer at UBS, Aug 2024 - Present · 2 yrs 1 mo, Pune District, Maharashtra, India, • Building a cloud-native data mesh architecture on Azure for Wealth Management post-trade operations. • Developing Java and Kotlin/Spring Boot microservices for scalable data and report generation services. • Provisioned 100+ Azure resources using Terraform and Ansible, reducing environment setup time by 80%. • Automated GitLab CI/CD pipelines, reducing manual deployment effort by 50% and accelerating release cycles. • Implemented IAM-based authentication for secure service communication adhering to banking security standards. • Enhanced system reliability with Splunk and BigPanda monitoring for proactive incident management. • Awarded UBS Gold Certified Engineer and Differentiator Award for impact and innovation.

Software Engineer Intern at UBS, Jan 2024 - Aug 2024 · 8 mos, Pune District, Maharashtra, India, • Developed microservices using Java and Spring Boot to support real-time trade processing pipelines handling high-throughput financial data at UBS Wealth Management. • Built and integrated Apache Kafka event-streaming solutions to enable reliable, low-latency data flow between upstream trading systems and downstream post-trade processing services. • Collaborated with senior engineers on cloud infrastructure using Azure, contributing to the migration of legacy systems to cloud-native architecture. • Gained exposure to GitLab CI/CD pipelines, participating in automated testing and deployment workflows in an Agile development environment.

Software Development Intern at Ford Pro, Jun 2023 - Dec 2023 · 7 mos, Bengaluru, Karnataka, India, - Worked extensively with Large Language Models (LLMs) for various tasks. - Implemented efficient methods for storing and managing data related to LLMs. - Automated the bug reporting process to streamline development workflows. - Successfully trained a chatbot using diverse LLM architectures. - Conducted thorough data analysis to generate insightful reports for multiple projects.

Software Engineer Intern at Ford Pro, Jun 2023 - Dec 2023 · 7 mos, Developed LLM-powered internal tools that automated bug reporting and streamlined resolution, improving triage efficiency. Built and deployed a conversational chatbot that helped users diagnose and resolve issues in internal applications.

Android App Development and Machine Learning Intern at ICAR- Indian Council of Agricultural Research, Jun 2022 - Aug 2022 · 3 mos, New Delhi, Delhi, India, - Trained a machine learning model specifically designed to detect diseases in wheat plants. - Developed a user-friendly mobile application that seamlessly integrates the machine learning model. - Implemented the machine learning model within the mobile app to provide intuitive disease detection capabilities for users. - Ensured accessibility and ease of use in the mobile app interface to cater to a wide range of users, including farmers and agricultural professionals.

Machine Learning Intern at ICAR-National Institute of Agricultural Economics and Policy Research, Jun 2022 - Aug 2022 · 3 mos, Trained a CNN model (Keras/TensorFlow) to detect wheat-crop diseases and shipped an Android app for in-field diagnosis; work published in Procedia Computer Science (Elsevier, 2024).

Education

PGDM, Finance, XLRI Jamshedpur

Bachelor of Technology - BTech, Computer Science, Shiv Nadar University

Public links

github: https://github.com/SamyakJ05