Stack Developer Machine Learning

MRUDULA NIMMALA

Location: Hayward, CA Phone: 510-***-**** Email: ***************@*****.***

SUMMARY

Full Stack Developer and AI/ML Practitioner with over 5+ years of experience designing scalable web applications using Java, Spring Boot, and React.

Proficient in developing and integrating RESTful APIs and microservices, with expertise in cloud platforms like AWS and IAM solutions (AWS).

Skilled in Python-based machine learning and NLP, delivering data-driven solutions with statistical modeling and advanced analytics.

Experienced in Agile/Scrum environments, leveraging DevOps tools (Git, Jenkins, Docker, Kubernetes) for efficient development and deployment.

Adept with SQL/NoSQL databases (MySQL, PostgreSQL, MongoDB) and security protocols (OAuth 2.0, OpenID Connect, JWT, SSO, MFA).

TECHNICAL SKILLS

Languages: Java 8+, Java 17, Python, SQL, R programming.

Frameworks/Technologies: Spring Boot, Spring Security, Hibernate, Apache Camel, React.js, Maven, RESTful & SOAP APIs.

AI/ML & Data Science: scikit-learn, pandas, matplotlib, seaborn, Plotly, spaCy, BERT, TensorFlow, logistic regression.

DevOps & CI/CD: Git, SVN, Jenkins, GitHub Actions, Docker, Kubernetes, YAML configuration.

Cloud & Tools: AWS (S3, EC2, Lambda), OpenShift, Postman, Swagger (OpenAPI), Splunk, Instana.

Databases: MySQL, Oracle SQL Developer, MongoDB, PostgreSQL.

Security: OAuth 2.0, OpenID Connect, JWT, SSO, MFA, secure coding principles.

Testing: JUnit 5, Power Mockito, SonarQube.

ORM Frameworks: Proficiency in Hibernate and similar ORM frameworks.

PROFESSIONAL EXPERIENCE:

Java Full Stack Developer JPMC – New York, NY

Jan 2025 – Present

Develop secure, scalable Java/J2EE applications using Spring Boot and Spring Security, integrating IAM solutions for enterprise systems in an Agile/Scrum environment.

Enhance application security with OAuth 2.0, OpenID Connect, and JWT, supporting SSO and MFA functionalities for robust user access control.

Design and implement Java-based applications using Spring Boot, Spring Security, and Hibernate/JPA for efficient data handling and secure transactions.

Integrate with IAM platforms (e.g., Okta, Azure AD) to enable seamless SSO and MFA, improving user authentication workflows.

Develop and optimize RESTful APIs and SOAP services, ensuring interoperability with enterprise systems and third-party platforms.

Utilize Git, Maven, Jenkins, and JIRA for streamlined version control, build automation, and project management.

Work with MySQL and Oracle databases, optimizing queries for enhanced performance and data integrity.

Apply DevSecOps practices, incorporating secure coding standards and Docker for containerized deployments.

Collaborate with cross-functional teams to deliver features on time, adhering to Agile sprint cycles.

Java Developer Infosys – India

Apr 2022 – Jan 2023

Built and maintained Java-based e-commerce applications for TotalEnergies, leveraging Spring Boot and Hibernate to support transaction and order management.

Worked in an Agile environment, ensuring high-quality microservices delivery with performance monitoring and code quality tools like Instana, Splunk, and SonarQube.

Developed Spring Boot microservices for e-commerce functionalities, including transaction processing and order management, enhancing system reliability.

Integrated Hibernate/JPA for efficient data persistence, optimizing SQL queries to improve database performance.

Monitored application health using Instana and Splunk, proactively resolving performance bottlenecks to ensure consistent uptime.

Enforced coding standards with SonarQube, maintaining compliance with coding best practices.

Collaborated with Agile teams to deliver critical features on schedule, improving client satisfaction.

Automated build and deployment processes using Maven, reducing manual overhead.

Documented APIs using Swagger, facilitating seamless integration for development teams.

Java Developer Capgemini – India

Nov 2018 –Mar 2022

Developed secure APIs and microservices for fraud detection and customer data integration, utilizing Spring Boot, OAuth, and AWS technologies.

Configured Kubernetes deployments and real-time data pipelines, enhancing application scalability and performance in Agile environments.

Built fraud detection APIs using AuthenticID, LexisNexis, and OTP verification, strengthening application security.

Implemented OAuth-based authentication to ensure secure user access across platforms.

Integrated customer data with NetCracker ICOMS, documenting APIs using Swagger for clarity and reusability.

Deployed services on AWS Snowflake and CodeCommit, monitoring performance with Splunk to maintain system reliability.

Configured Kubernetes pods using YAML for development, staging, and production environments, ensuring scalable deployments.

Designed SOAP-based services for non-pay enhancements using Spring Boot and Oracle SQL Developer.

Developed real-time data pipelines with Apache Kafka, enabling efficient data processing.

Automated testing with JUnit5 and PowerMockito, ensuring robust integration validation.

AI & MACHINE LEARNING PROJECTS

UFO Sightings Near Area 51 (Data Science + Visualization)

Decoding the Mystery of UFO Sightings: Patterns and Insights Near Area 51

Python, pandas, matplotlib, seaborn, Plotly, geospatial analysis, temporal analysis

Analyzed 80,000+ UFO sighting records near Area 51 using spatial and temporal techniques.

Built interactive visualizations (contour density plots, time-series, and heatmaps) revealing high-density triangular clusters near Indian Springs, Rachel, and Ash Springs.

Identified significant patterns in sighting frequency based on time (8–10 PM), season (July), and location.

Delivered actionable insights into global sighting trends, showcasing strong data storytelling skills using Python-based visualizations.

Exoplanet Habitual Zone Detection (ML Models in R)

Exoplanet Habitual Zone Classification

R, logistic regression, random forest, lasso regression, ggplot2

Built machine learning models to predict whether exoplanets fall within the habitable zone using logistic regression, random forest, and LASSO regression.

Achieved 96.4% accuracy and AUC of 0.95 with reduced logistic model after VIF-based feature selection.

Implemented ROC curves, confusion matrices, and cross-validation to validate model performance.

Demonstrated feature importance through random forest and regularization via LASSO to avoid overfitting.

EDUCATION

Masters in Statistics, Data Science Concentration

California State University, East Bay Hayward, CA

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