Software Developer

Harsha Mohan Paladugu

Phone: 510-***-**** Email: **************@*****.*** LinkedIn: harsha-mohan-paladugu Summary:

Experienced Software Developer with 6+ years of expertise in backend engineering using Java, Golang, and Python, specializing in building scalable REST APIs and real-time data pipelines with Spring Boot, Gin, Flask, Apache Flink, and Kafka. Proven track record in implementing multithreaded systems, optimizing performance with concurrency patterns, and deploying containerized microservices via Docker and Kubernetes. Skilled in caching strategies with Redis, relational database design with PostgreSQL, and secure authentication using OAuth 2.0. Adept at leading teams, applying design patterns like Singleton, Builder, and Factory Method, and ensuring observability with Splunk and Grafana across distributed, cloud-native environments.

Skills

● Languages & Frameworks : Java (8+), Golang, Python, Spring Boot, Flask, Gin, SQL, JavaScript

● Databases: PostgreSQL, Redis, MySQL, SQLAlchemy, LDAP

● Stream & Batch Processing: Apache Flink, Kafka, PySpark

● Machine Learning: TensorFlow, Scikit-learn, OpenCV, Keras, Pandas, NumPy

● Tools: Docker, Kubernetes, Git, Logrus, JUnit, Testify, Pytest

● Monitoring & Logging: Splunk, Grafana

● Cloud Platforms: AWS (S3, EC2,EKS, Lambda, Cloud Watch, ELB), Google Cloud Platform

● Design Patterns: Singleton, Builder, Factory Method Professional Experience

Apple

Software Engineer June 2021 – Present

● Developed high-performance microservices using Java (Spring Boot), Golang (Gin), and Python

(Flask) to expose secure, scalable APIs for internal and customer-facing platforms.

● Leveraged Java multithreading with ExecutorService, thread pools, and concurrency-safe collections to parallelize I/O-heavy workloads, ensuring responsive APIs under high concurrency.

● Developed RESTful APIs using Java and Spring Boot with Spring MVC architecture, applying clean controller-service-repository separation and OpenAPI documentation.

● Implemented asynchronous workflows in Spring Boot using CompletableFuture, and custom thread pools (ExecutorService) to handle non-blocking tasks and optimize API responsiveness under high concurrency

● Designed distributed Apache Flink pipelines for both real-time and batch processing; implemented modular Flink jobs with checkpointing, parallel task slots, custom windowing strategies, and high-throughput Kafka integration.

● Engineered OAuth 2.0 authentication across APIs using JWT tokens and custom validation middleware, ensuring secure and stateless access for millions of requests per day.

● Built async REST endpoints in Gin (Go) with pgx PostgreSQL integration and Redis caching for volatile metadata, optimizing data fetch latency and throughput in high-volume request patterns.

● Built high-performance REST APIs using Golang with the Gin framework, leveraging pgx connection pooling for PostgreSQL interaction and minimizing latency under high request volumes.

● Designed reusable data access layers in Go using pgxpool, ensuring connection safety, transaction management, and graceful error handling across distributed microservices.

● Tuned Golang applications using GOMEMLIMIT, pprof profiling, and heap snapshot analysis to prevent memory leaks and reduce container restarts under load spikes.

● Built Flask-based APIs to serve customized data queries using SQLAlchemy ORM with PostgreSQL, enabling dynamic filtering, pagination, and optimized query generation for analytics and reporting use cases.

● Developed Flink batch jobs orchestrated by Airflow for periodic reconciliation, backfills, and denormalized metric generation with exponential retry logic and alerting on failure events.

● Containerized all services with Docker and deployed on Kubernetes, managing resources through CronJobs, Horizontal Pod Autoscalers, and rollout policies defined via declarative manifests.

● Designed optimized relational schemas in PostgreSQL and improved query efficiency by 30%+ through indexing strategies, materialized views, and SQL plan tuning.

● Applied Singleton pattern for SOAP service client instantiation with built-in rate-limiting; used Builder pattern to construct configurable Flink clients with retry support; implemented Factory Method to dynamically select Flink job logic based on Kafka topic.

● Established structured logging standards using Logrus (Go) and SLF4J (Java) with embedded jobId, requestId, and orgId to support distributed tracing and log correlation.

● Built production observability dashboards in Grafana and integrated alerting pipelines with Splunk, capturing system KPIs, error rates, and memory usage trends across services.

● Integrated development workflows with AI-powered Large Language Models (LLMs) to automate initial code review and provide real-time feedback on logic, syntax, and design patterns, accelerating peer review cycles and improving code quality.

● Accelerated rapid backend development by integrating AI-powered LLM tools into the engineering workflow for code generation, review suggestions, and boilerplate automation—reducing development turnaround time and enhancing initial code quality.

● Maintained API contracts using OpenAPI specs, enabling automated validation, mock server generation, and seamless frontend-backend integration.

● Mentored junior developers on concurrency, Flink architecture, error handling best practices, and observability, leading to improved code quality and reduced production incidents.

● Collaborated with DevOps, security, and platform teams to meet SLOs, implement fine-grained access controls, and maintain 99.9% service availability across critical workloads. Aadhya Analytics

Software Engineer Oct 2018 – June 2019

● Built a computer vision PoC called Data2Insights using TensorFlow Object Detection API for identifying brands and human presence across video feeds.

● Applied machine learning principles including transfer learning and bounding box optimization using OpenCV and Scikit-learn.

● Trained models on AWS EC2 GPU-enabled instances, automated result exports to S3 buckets for scalable storage and access.

● Used Pandas, NumPy, and Matplotlib for data preprocessing and visualization; reduced model drift through normalization and augmentation.

● Integrated ML outputs with Flask APIs for model inferencing and reporting. MG Infomatics

Python Developer June 2017 – Sep 2018

● Developed visual search engine using Bag of Visual Words and HOG for object retrieval based on feature clustering; indexed data in Redis.

● Built real-time face detection models using OpenCV and CNN-based classifiers; exposed bounding box predictions via REST APIs.

● Created automated web scrapers to build custom datasets; stored and cleaned image metadata in MySQL.

● Contributed to full-stack implementation using HTML5, CSS3, and Flask. Followed SDLC, UML documentation, and test-driven development using JUnit. Education

Masters of Science in Computer Science

● Southeast Missouri State University, MO, USA GPA: 4.0 Aug 2019 – May 2021 Bachelor of Technology in Electronics & Communication Engineering

● JNTU Kakinada, India GPA: Oct 2013 – May 2017 Projects

Heart Disease Prediction

● Preprocessed UCI dataset using Pandas & NumPy. Evaluated ML models: Logistic Regression, SVM, Clustering, Stacking, and Neural Networks.

COVID-19 Case Analysis

● Cleaned and aggregated large data to analyze demographics, symptoms, and spread. Built visual insights using Matplotlib.

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