Abhishek Singh
About Me
I find myself a curious mind with a deep interest in distributed systems, design patterns, and writing clean, maintainable code with zero technical debt because I believe technical debt is the worst enemy of any engineer. I recently earned my Master’s in Computer Science from California State University. I love automating every boring and repetitive task, turning challenges into opportunities, and being outdoors when away from the keyboard.
Projects
Suzess is a unified streaming recommendation platform featuring a Chrome extension and web dashboard that delivers real-time, personalized movie and show suggestions across Netflix, Prime Video, Hulu, and more, tailored as per the user's subscriptions and preferences.
Built a real-time stock market analytics platform using Python, Flask, Kafka, and Spark, reducing data delays from 8s to 3s and boosting throughput by 60%. Leveraged Chart.js for interactive visualizations, enabling users to track live market trends with engaging, low-latency dashboards
LinkedIn Email Extractor (LEE) is a Python script that uses Google’s Custom Search Engine API to find LinkedIn profiles and automatically extract email addresses from profile descriptions. Open-sourced under the MIT license, LEE has earned 60 GitHub stars and 47 forks to date.
Skills
Languages
Java
Python
Javascript
HTML
CSS
YAML
BashDatabases & Messaging
PostgreSQL
MongoDB
Redis
Apache KafkaInfrastructure & Tools
AWS
Jenkins
Docker
Terraform
Teamcity
Jira
Git
Maven
KubernetesFrameworks
Spring
JUnit
Spark
Hadoop
Selenium
Appium
Karate
CucumberLibraries
React
NextjsWork Experience
- Use monitoring tools to keep track of system performance and identify any signs of issues.
- Ensure accurate and timely documentation of all incidents and interactions in the ticketing system.
- Support the implementation of asset management policies and procedures under the guidance of the IT Asset Inventory department.
- Ensure compliance with operational and security protocols to maintain system integrity and data confidentiality.
- Led a team to implement continuous code quality improvements, reducing technical debt and aligning with best practices through static code analysis.
- Contributed to developing scalable, high-traffic services within a microservices-based architecture, supporting seamless user experiences.
- Strengthened application security and compliance by integrating standardized authentication mechanisms and ensuring alignment with data protection.
- Enhanced delivery efficiency and release reliability through CI/CD pipeline automation using container orchestration and cloud-native tooling.
- Participated in Agile ceremonies within cross-functional teams to drive iterative development, improve collaboration, and ensure timed delivery.
- Contributed to the end-to-end engineering of distributed, multi-tenant applications by aligning backend architecture with domain-driven design (DDD).
- Improved system throughput and reduced latency by applying database normalization, query optimization, and caching strategies.
- Ensured service resilience and fault isolation in event-driven microservices by incorporating circuit breakers, rate limiting, and observability.
- Employed engineering standards, including SOLID principles, DRY/KISS practices, and test-driven development (TDD), leading to improved maintainability
- Collaborated in Agile delivery cycles with continuous integration workflows (TeamCity, Git) to support iterative releases and platform stability.
Education
Specialized in distributed systems and database management.
Core curriculum in algorithms, data structures, and systems programming.
Certifications
Oracle
Scored 90% on the Oracle Java certification exam, demonstrating my proficiency in encapsulation, conditional logic, multi-dimensional arrays, and operator precedence.
Amazon Web Services
Demonstrated expertise in designing and deploying scalable, highly available, and cost-efficient systems on AWS. Gained hands-on experience with cloud architecture best practices, security, and performance optimization.
Amazon Web Services
Gained foundational knowledge of generative AI concepts, including large language models (LLMs), responsible AI practices, and common use cases. Explored real-world applications and AWS tools used in building generative AI solutions.