Software developer

PAYOSHNI KHOKALE

*****************@*****.*** +353-********* www.linkedin.com/in/payoshni8 Stamp 1G PROFESSIONAL SUMMARY

Backend Software Engineer with 3+ years of experience designing scalable, distributed systems and high-availability REST APIs. Strong expertise in Python, microservices architecture, system reliability, performance optimization, and cloud deployments. Experienced in building large-scale backend services handling high concurrency, secure authentication, and enterprise-grade production environment.

PROFESSIONAL EXPERIENCE

Assistant Coordinator – Mapfre, Ireland Aug 2025 – Present

• Designed and maintained Python (Flask)-based backend services supporting policy servicing and claims processing systems used by internal operations teams.

• Improved API response time by 30% by implementing SQL indexing strategies, optimizing complex joins, and adding service-layer input validation.

• Strengthened authentication and session validation using token-based middleware checks, reducing login-related production incidents by 20%.

• Implemented centralized structured logging and log analysis workflows, reducing Mean Time to Resolution (MTTR) by 25%.

• Collaborated with product owners, QA engineers, and frontend teams during sprint planning and release cycles to ensure stable bi-weekly deployments.

• Resolved recurring production issues caused by malformed customer data, improving API reliability.

Software Engineer – Cognizant, India Nov 2021 – Jun 2024

• Engineered scalable Order Management & Fulfillment backend platform using Python

(Flask) microservices for a global retail enterprise serving 500K+ monthly users.

• Built REST APIs for order creation, inventory validation, payment processing, and shipment orchestration, reducing checkout time by 18%.

• Optimized high-concurrency endpoints using database indexing and caching, improving

• peak-load throughput by 25%.

• Implemented asynchronous background processing for order status updates, reducing API latency by 15%.

• Integrated third-party payment gateways and logistics APIs with retry logic and fault- tolerant error handling.

• Improved unit and integration test coverage, reducing production defects by 20%.

• Deployed containerized microservices via CI/CD pipelines enabling zero-downtime releases.

• Enhanced backend architecture focusing on scalability, idempotent APIs, and long-term maintainability.

PROJECTS

Amazon Fraud Detection using GANs

• Designed fraud detection system to handle class imbalance using synthetic data generation.

• Improved fraud recall by 18% compared to baseline model.

• Built modular ML pipeline for preprocessing, training, and evaluation. Stock Market Sentiment Analysis using LSTM

• Developed NLP pipeline for financial sentiment prediction using LSTM networks.

• Improved prediction stability by 15% through feature engineering and tuning.

• Designed scalable processing pipeline for structured and unstructured datasets. TECHNICAL SKILLS

• Languages: Python, Java

• Backend: REST APIs, Flask, Microservices, Distributed Systems

• Databases: SQL, MySQL, Indexing & Query Optimization

• Cloud: Azure (App Services, Blob Storage), AWS (EC2, S3)

• DevOps: CI/CD Pipelines, Docker, Git

• Monitoring: Structured Logging, Debugging, Performance Tuning

• Practices: Agile, Code Reviews, Unit & Integration Testing EDUCATION

University of Galway

M.Sc in Computer Science (Artificial Intelligence) 2024 – 2025 Relevant Modules: Machine Learning, Deep Learning, AI Ethics, NLP, Information Retrieval, Knowledge Graphs, Agents & Reinforcement Learning, Research Topics in AI Sipna College of Engineering & Technology

Bachelor of Computer Science & Engineering 2018 – 2021 Relevant Modules: Data Structures & Algorithms, Operating Systems, Database Management Systems, Object-Oriented Programming, Software Engineering, Theory of Computation LEADERSHIP & EXTRA-CURRICULAR

Vice Auditor – AI Society, University of Galway 2024 – 2025

• Organized AI-focused events and hackathons in collaboration with students, academics, and multidisciplinary teams.

• Promoted discussion on emerging AI topics, innovation, and responsible AI practices.

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