Venkata P
San Francisco,CA
Summary
Senior Full Stack Software Engineer with 5+ years of experience building enterprise-scale, cloud-native applications integrated with AI and Machine Learning capabilities. Strong expertise in Java, Python, Spring Boot, Microservices Architecture, and RESTful APIs, delivering high-volume, real-time systems across Banking, Financial Services, Insurance, and Healthcare domains. Hands-on experience developing fraud detection, predictive analytics, anomaly detection, recommendation systems, and NLP solutions, with end-to-end ML pipeline development using Scikit-learn, TensorFlow, Keras, Pandas, and NumPy, covering data preprocessing, feature engineering, model training, evaluation, and real-time deployment via FastAPI and Spring Boot APIs. Proficient in AWS (EC2, S3, RDS, Lambda, IAM) and Azure (App Services, Azure SQL, Blob Storage, Key Vault), building scalable cloud-native systems using Docker and Kubernetes. Experienced across the full SDLC including system design, API architecture, CI/CD automation, cloud deployment, and performance optimization within Agile environments, with strong foundations in OAuth2, JWT, RBAC security, DevOps, and MLOps, delivering secure, high-performance AI-enabled solutions aligned with business objectives.
Overview
8
8
years of professional experience
Work History
Software Engineer
Goldman Sachs
United States
02.2022 - Current
- Led architecture of AI-enabled microservices using Java, Spring Boot, and AWS, defining service boundaries and scalability strategies to process millions of financial transactions daily with 200ms latency and 99.9% availability.
- Designed and productionized Fraud Detection and Risk Scoring ML models using TensorFlow and Scikit-learn, improving precision by 12–18% through advanced feature engineering and hyperparameter tuning.
- Built end-to-end MLOps pipelines leveraging AWS S3, Glue, EMR, Lambda, and Step Functions for multi-terabyte data ingestion, model lifecycle management, validation, versioning, and regulatory audit traceability.
- Architected event-driven distributed systems using MSK (Kafka), SQS, SNS, and EventBridge, ensuring high-throughput, horizontally scalable, fault-tolerant real-time processing.
- Designed and optimized database schemas, indexing strategies, and transaction management across RDS/PostgreSQL and DynamoDB, ensuring ACID compliance and high-concurrency performance.
- Improved backend performance by 25% through JVM tuning, garbage collection optimization, Redis (ElastiCache) caching, SQL optimization, connection pooling, and load balancing (ALB/NLB).
- Implemented resilient design patterns including circuit breakers, retries, idempotent processing, and dead-letter queues to enhance system stability during peak transaction loads.
- Owned cloud-native deployments using Docker, Kubernetes (EKS), Terraform, and CloudFormation, enabling horizontal auto-scaling and reducing release cycles by ~50% with zero-downtime rollouts.
- Strengthened enterprise security using Spring Security, OAuth2, JWT, integrated with AWS IAM, KMS, and Secrets Manager, ensuring compliance with financial governance standards.
- Built and optimized automated CI/CD pipelines (Jenkins, GitHub Actions, CodeBuild, CodeDeploy, ECR) and implemented observability using CloudWatch and AWS X-Ray, leading RCA initiatives that reduced incident response time by 30%.
Software Engineer
Capgemini
INDIA
03.2018 - 11.2019
- Developed high-performance backend systems using Modern C++ (C++11/14) with STL, Smart Pointers, RAII, Move Semantics, and Multithreading (std::thread, mutex, atomic) to build memory-safe, scalable enterprise modules.
- Applied strong Core Java fundamentals including Collections Framework, Generics, Concurrency (ExecutorService, Synchronization), JVM Memory Model, and Exception Handling to build robust concurrent backend services.
- Designed and implemented RESTful APIs using Spring Boot, Spring MVC, Hibernate, and JPA, supporting scalable Service-Oriented Architecture (SOA).
- Optimized Algorithms & Data Structures through time/space complexity analysis, improving system throughput by ~15% with efficient memory management.
- Integrated C++ performance-critical modules with Java-based Microservices, enabling interoperability across Distributed Systems.
- Deployed and managed production workloads on Microsoft Azure (App Services, Virtual Machines, Azure SQL, Cosmos DB, Blob Storage) and containerized services using Docker and Azure Kubernetes Service (AKS).
- Built automated CI/CD pipelines using Azure DevOps, reducing release cycle time by ~40% and improving deployment reliability.
- Implemented secure communication using HTTPS, Token-Based Authentication, RBAC, and enhanced observability via Azure Monitor and Application Insights, following SOLID principles, Clean Architecture, and Design Patterns (Factory, Strategy, Singleton).
Education
Master of Science - Computer Science
George Mason University
Virginia12-2021
Skills
TECHNICAL SKILLS
Languages: Java, Python, C, JavaScript, TypeScript, SQL, Bash
Backend: Spring Boot, Spring Security, Hibernate, JPA, Nodejs, Expressjs, FastAPI
Frontend: Reactjs, Angular, HTML5, CSS3
Architecture: Microservices, REST APIs, Event-Driven Systems (Kafka/MSK), Distributed Systems, API Gateway
AI/ML: TensorFlow, Scikit-learn, Keras, PyTorch, Pandas, NumPy, NLP, Predictive Modeling, MLOps
Databases: PostgreSQL, MySQL, Oracle, SQL Server, MongoDB, DynamoDB
Cloud:
AWS (EC2, S3, RDS, Lambda, EMR, Glue, MSK, Step Functions, IAM, CloudWatch)
Azure (App Services, Azure SQL, Blob Storage, AKS, Key Vault)
DevOps: Docker, Kubernetes (EKS/AKS), Terraform, CloudFormation, Jenkins, GitHub Actions, Azure DevOps
Testing: JUnit, Mockito, PyTest, Selenium
Timeline
Software Engineer
Goldman Sachs
02.2022 - Current
Software Engineer
Capgemini
03.2018 - 11.2019
Master of Science - Computer Science
George Mason University