Software Engineer C# .net

SHIVA TEJA JALA

Las Vegas, NV 702-***-**** ***********@*****.*** LinkedIn

SUMMARY

Results-driven Software Engineer with 3+ years of experience in designing, developing, and deploying scalable backend systems and APIs using Python, C#, and modern frameworks like FastAPI, Flask, and ASP.NET Core across finance and EdTech domains.

Skilled in building robust microservices architectures and event-driven systems, with hands-on experience in containerization (Docker, Kubernetes), cloud infrastructure (AWS, Azure), and CI/CD pipeline automation.

Strong background in integrating AI/ML models into production workflows, working with tools like TensorFlow, Scikit-learn, and PySpark, and optimizing MLOps practices in high-volume data environments.

Adept at collaborating with cross-functional teams to implement secure, modular, and maintainable codebases, with deep knowledge of RESTful API design, asynchronous task queues, and API documentation standards

Experienced in leveraging relational and NoSQL databases (PostgreSQL, MongoDB, SQL Server), implementing schema design, performance tuning, and data orchestration for real-time applications.

Proven ability to operate in Agile environments, contributing to full SDLC processes, participating in code reviews and sprint planning, and maintaining high standards for code quality, testability, and system observability.

SKILLS

Programming Languages: C#, Python, C++, JavaScript, Typescript

Frameworks & Libraries: Flask, Django, FastAPI, Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch, OpenCV, XGBoost, Keras

Web Technologies: HTML5, CSS3, RESTful APIs, WebSockets, Microservices, JSON, PHP, Bootstrap, jQuery, React, WPF, Angular

Cloud Platforms: AWS (Lambda, S3, Rekognition, DynamoDB, EC2, Step functions, AWS Gateway, IAM), Microsoft Azure (Data Factory, Azure Synapse Analytics, DataBricks, Functions, Service Bus, PowerApps, Blob Storage, Azure Logic Apps, Azure App Insights)

DevOps & CI/CD: Docker, Kubernetes, Jenkins, Terraform, Ansible, Azure DevOps, Jira, Microsoft Team Foundation Server, NPM

AI/ML & Data Science: Supervised & Unsupervised Learning, Deep Learning, Model Tuning (GridSearchCV, RandomSearch), Feature Engineering, NLP (spaCy, NLTK), Computer Vision

Databases and ORM: Microsoft SQL Server, Oracle, MongoDB, Postgre SQL, T-SQL, CosmoDB, GraphQL, Entity Framework

Version Control: Git, GitHub, Bitbucket, GitLab

Testing & QA: PyTest, UnitTest, Selenium, Postman, JMeter

Agile & Project Tools: Agile, Scrum, JIRA, Confluence, Trello, ClickUp

Security & Compliance: JWT, OAuth2, HTTPS, OWASP Top 10, Role-Based Access Control (RBAC), Encryption Standards

Monitoring & Logging: Nagios, Prometheus, Splunk, Grafana, Datadog

Soft Skills: Strong Analytical Thinking, Problem Solving, Effective Communication, Adaptability, Team Collaboration, Critical Thinking, Attention to Detail, Time Management, Continuous Learning, Technical Documentation, API Documentation

WORK EXPERIENCE

Wells Fargo Las Vegas, NV

Software Engineer Nov 2024 – Present

Developed a dynamic pricing engine using .NET 8, C#, and MongoDB, supporting real-time rate and eligibility computations across loan origination platforms; enabled intelligent rule evaluation for thousands of configurable pricing scenarios.

Built scalable RESTful APIs with ASP.NET Core and Entity Framework, integrating pricing services with booking, audit, and return modules; optimized LINQ queries to reduce data retrieval time during peak usage

Integrated Apache Kafka to enable asynchronous message handling across distributed services, ensuring real-time propagation of pricing decisions and seamless communication with downstream systems.

Designed efficient MongoDB schemas and indexing strategies tailored for rule-based matching and rapid decisioning under high-throughput workloads.

Collaborated with data science teams to incorporate AI-driven rate optimization models into pricing workflows; integrated predictive insights for borrower segmentation and eligibility scoring, enhancing pricing precision.

Developed a high-volume testing framework in .NET to simulate concurrent API traffic, validate Kafka message consistency, and ensure robustness of AI-injected pricing logic under load.

Leveraged Azure Application Insights and Splunk to build real-time monitoring dashboards and custom alerts; improved system observability and reduced pricing API latency by 35%.

Automated data ingestion pipelines using Azure Data Factory and visualized key pricing metrics through Power BI; enabled business stakeholders to track AI model performance and identify trends in pricing behavior.

Maintained CI/CD processes in Azure DevOps and collaborated in Agile ceremonies via Jira and Confluence; ensured full traceability and version control of pricing rules and AI model configurations for regulatory audits.

Capital One Las Vegas, NV

Software Engineer Sept 2023 - Oct 2024

Designed and modularized Python-based components of rubicon-ml, enabling the tracking and management of over 10,000 machine learning experiment runs quarterly across fraud and credit risk models, ensuring model reproducibility and audit compliance.

Developed scalable REST APIs using FastAPI to retrieve and filter experiment metadata stored in AWS S3, reducing model tuning time by 40% through real-time access to parameters, metrics, and training artifacts.

Integrated AWS S3 as the central metadata store using Boto3, implementing logic for duplication, versioning, and fault tolerance, supporting over 500GB of daily experiment data with role-based access control.

Containerized the rubicon-ml application using Docker and managed infrastructure provisioning with Terraform, standardizing deployments across EC2 and Databricks environments, which reduced environment setup time by 90%

Automated testing and deployment using Jenkins and GitHub Actions, incorporating linting, unit tests, and container builds into the CI/CD pipeline, minimizing manual release errors and enabling biweekly production updates with zero downtime.

Enhanced compatibility with large-scale ML workflows by extending rubicon-ml’s functionality to support PySpark-based experiment logging in Databricks, enabling centralized tracking of distributed training jobs.

Implemented Git-integrated version control within rubicon-ml to embed commit references in experiment logs, improving debugging accuracy and reducing validation cycle times by 25%.

Drove adoption by contributing new features such as CLI support and metadata search filters, and by improving documentation; these efforts led to a 3x increase in active users across Capital One’s ML teams.

Billion Apps Hyderabad, India

Associate Software Developer Jan 2021 – Mar 2022

Developed scalable RESTful APIs using Python (Flask) to manage course content, assessments, and user activity, improving API response times by 28% and supporting seamless access for over 50,000 concurrent users.

Designed and optimized relational database schemas in PostgreSQL for managing structured educational data, reducing complex query execution time by 35% and enabling efficient reporting for educators.

Implemented MongoDB to manage semi-structured datasets, including user interaction logs and feedback, improving real-time dashboard performance and supporting adaptive content delivery.

Collaborated with the AI/ML team to integrate a Python-based content recommendation engine, delivering personalized learning paths based on usage patterns, which increased user engagement and course completion by over 20%.

Built asynchronous task queues using Celery and Redis for operations like grading, notifications, and batch processing, reducing synchronous load and improving system responsiveness under high traffic.

Containerized microservices using Docker and deployed via Kubernetes on AWS and on-prem infrastructure, enabling automated scaling and achieving 99.9% system uptime during exam periods.

Developed automated testing pipelines using PyTest and GitLab CI, maintaining 92% test coverage and significantly reducing regression bugs during sprint deployments.

Monitored system health and performance using Prometheus and Grafana, identifying and resolving memory leaks and CPU spikes prior to rollout, preventing service interruptions for over 5,000 daily users.

Authored detailed API documentation, architecture diagrams, and data flow specifications in Confluence, supporting smoother cross-team integration and reducing onboarding time for new developers.

Participated in Agile ceremonies including sprint planning, code reviews, and retrospectives, contributing to six successful release cycles and improving sprint velocity by 25%.

EDUCATION

Master of Science in Computer Science

University of Bridgeport, Connecticut, USA

Bachelor of Technology in Computer Science and Engineering

Jawaharlal Nehru Technological University, Hyderabad, India

CERTIFICATIONS

SnowPro Core Certification

Certified Databricks Professional

Associate Microsoft Certified: Azure

Alteryx Foundational Micro-Credential

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