Hi, I'm
Sreekar Reddy Aparacherla
MBA Candidate (Operations) at Chicago Booth · Full Stack Engineer
Seasoned full-stack engineer with 7+ years of experience working on enterprise-grade distributed systems. Currently pursuing an MBA with a concentration in Operations at the University of Chicago Booth School of Business, combining deep technical expertise with business acumen to bridge engineering and strategy.
Experience
Equinix
Singapore
Multinational data center and colocation provider for enterprises
Staff Engineer
November 2022 – August 2025- Architected and optimized distributed backend services using Spring Boot REST APIs to facilitate seamless data orchestration between back-office systems, enhancing inter-service communication and high-throughput ordering.
- Operationalized a Digital Letter of Authorization (DLOA) microservice to automate cross-connect provisioning, increasing transaction throughput by 50% and eliminating manual validation latency.
- Leveraged distributed tracing (Zipkin) and log aggregation (Splunk) to identify performance bottlenecks and optimize latency in the customer portal, achieving ~20% faster response times and 99.99% availability.
Full Stack Engineer III
March 2021 – November 2022- Engineered customer-facing modules using ReactJS, NodeJS, and a micro frontend (MFE) architecture, optimizing state management and component reusability for a platform serving 50,000+ monthly active users.
- Improved system reliability and global latency by migrating and deploying containerized services on AWS cloud infrastructure, utilizing multi-region availability zones to ensure low-latency access.
- Automated CI/CD pipelines and Kubernetes/Rancher orchestration for server deployment, reducing manual overhead by 120 hours annually per team while maintaining high-availability (99.99% uptime) targets.
- Standardized technical interview rubrics for the 2022 engineering hiring cycle and mentored junior engineers/interns to maintain high code quality and architecture standards.
Capgemini
Singapore
Consulting and IT services company providing strategy, technology, and digital transformation services
Associate Consultant, Financial Services
August 2019 – October 2020- Modernized a legacy monolithic cashier system by developing a web-based wraparound application, abstracting complex banking logic behind a modern UI while maintaining system integrity.
- Developed middleware services to parse and transform legacy COBOL outputs into JSON/RESTful formats, enabling web-interface compatibility and extending the lifecycle of core banking systems.
Mega International
Singapore
Multinational SaaS company offering Enterprise Architecture and Governance, Risk & Compliance products
Solutions Engineer
April 2018 – July 2019- Deployed and configured HOPEX SaaS platform across enterprise client-server environments, managing load balancing and server-side configurations to support 100+ concurrent users.
- Automated asynchronous nightly cron jobs using VBScript to generate real-time data visualizations and notification triggers for risk-management modules, reducing manual reporting toil by 15 hours monthly.
Education
The University of Chicago Booth School of Business
Master of Business Administration
- Member of Booth Technology Group
- GRE: 334/340
National University of Singapore
Bachelor of Engineering (Chemical) With Honors
August 2013 – May 2017Singapore- NUS Science and Technology Scholarship
- Varsity Athlete and Team Manager (Cricket)
Skills
Languages
Frontend
Backend & APIs
Cloud & DevOps
Observability
Architecture
Additional
- Singapore Prisons Volunteer, supporting rehabilitation of inmates at the Drug Rehabilitation Center
- PADI certified open water diver; SSI certified advanced open water diver
- Finisher of the Singapore Marathon 2019
- Work authorized in the US via H1B1 (Singaporean citizens); not susceptible to H1B lottery
Certifications
ML Projects
Rudimentary implementations of ML algorithms, from scratch. No sklearn, just NumPy and Pandas.
Neural Network
Built a multi-layer neural network using only NumPy. No ML frameworks. Implemented forward propagation, ReLU activations, softmax output, and backpropagation by hand. Trained on MNIST to classify handwritten digits.
View Notebook →Decision Tree & Random Forest
Implemented a decision tree classifier from first principles using NumPy and Pandas, then extended it into a random forest with feature subsampling and bootstrap aggregation. Applied to the UCI Bank Marketing dataset to predict term deposit subscriptions.
View Notebook →Logistic Regression
Derived and implemented logistic regression with gradient descent using NumPy. Applied to Titanic survival prediction with extensive feature engineering: extracting titles from names, handling missing values, and encoding categoricals.
View Notebook →K-Means Clustering
Implemented K-Means clustering from scratch using NumPy. Applied to the Iris dataset to group plants by species without labels, using Euclidean distance and centroid updates. Evaluated cluster quality against ground-truth labels.
View Notebook →K-Nearest Neighbours
Implemented KNN from scratch using NumPy with Euclidean distance. Applied to the MovieLens dataset as a content-based movie recommender: given a film, finds the k most similar titles by genre and release date.
View Notebook →Linear Regression
Implemented multivariate linear regression using the closed-form normal equation with NumPy. Applied to the California Housing Prices dataset with feature normalization and matrix algebra. No sklearn involved.
View Notebook →