AI Software Solutions Architect with 7+ Years Experience

Mishan Parajuli

******************@*****.*** 504-***-**** LinkedIn

Professional Summary

AI Software Solutions Architect Engineer with 7+ years of experience building and modernizing full-stack, cloud-based applications using Java (8–21), Spring Boot, Python, and Angular (12-17). Strong background in microservices, event-driven systems, REST APIs, and AWS-based deployments, with recent hands-on experience delivering AI-powered solutions from proof-of-concept through production. Background includes full-stack development for retail platforms, payment systems, and healthcare applications, with a focus on scalability, security, and maintainability.

Technical Skills

Languages: Java (8–21), Python 3.x, JavaScript (ES6+), TypeScript, SQL Frameworks: Spring Boot (2.x/3.x), Spring Framework, Hibernate, Spring Batch, Angular (12–15), React Generative AI / ML: GenAI application development, LLM-based workflows (GPT-4, Claude), Prompt Engineering, Retrieval-Augmented Generation (RAG), LangChain, AI/ML service integration, MLOps

(deployment, monitoring, versioning), AWS SageMaker, ONNX Runtime, Agent-style decision services Cloud & DevOps: AWS (EC2, Lambda, ECS, RDS, S3, SQS, SNS, DynamoDB, API Gateway, CloudWatch, KMS), Docker, Jenkins, Terraform, GitHub Actions, AWS CodePipeline Architecture: Microservices, Event-Driven Architecture, REST APIs, Serverless patterns Databases: PostgreSQL, MySQL, DynamoDB

Security & Compliance: OAuth2, JWT, AES-256 encryption, HIPAA, HL7 Testing & Monitoring: JUnit, Mockito, SonarQube, CloudWatch, SLF4J, Logback Methodologies: Agile (Scrum, Kanban), CI/CD, DevSecOps, Code Reviews, Mentorship Professional Experience

PayPal – San Jose, CA June 2023 – Present

AI Software Solutions Architect Engineer

Architected scalable microservices and LLM based decision workflows using Java Spring Boot 3.x and Python across Kafka driven event systems and REST APIs, improving workflow automation accuracy by 30% and reducing manual review cycles.

Built LLM powered decision services using GPT 4, Claude, and Retrieval Augmented Generation with LangChain, integrating enterprise data sources and reducing AI response inconsistency by 35% in production.

Designed and implemented AI orchestration workflows using Semantic Kernel and LangChain to manage prompt pipelines, context injection, and tool calling, improving multi step reasoning reliability and reducing manual intervention by 40%.

Designed and operated MLOps inference pipelines using AWS SageMaker and ONNX Runtime supporting real time and batch predictions, increasing model deployment frequency by 50% while maintaining production stability.

Modernized core platform services by leading upgrade from Java 17 to Java 21, resolving dependency conflicts and improving application performance by 20% while strengthening long term platform support.

Provisioned and standardized AWS infrastructure using Terraform across ECS, Lambda, SQS, SNS, RDS, and S3, reducing environment drift and cutting deployment failures by 30%.

Developed Databricks based data pipelines to aggregate high volume Kafka event data, reducing AI feature preparation time by 25% and improving analytics readiness.

Enhanced system resilience by applying asynchronous microservices patterns and Resilience4j, reducing cascading service failures by 30% during peak traffic.

Strengthened platform security by implementing OAuth2, JWT authentication, and AWS KMS encryption, eliminating critical audit findings and improving compliance posture by 100% across evaluated services.

Implemented monitoring and observability using CloudWatch, SonarQube, SLF4J, and Logback, increasing production issue detection speed by 40% and supporting 99.9% availability.

Developed Angular and React dashboards to visualize AI outputs and system health metrics, reducing troubleshooting time for operations teams by 35%.

Streamlined CI CD pipelines using Jenkins and AWS CodePipeline, reducing release cycle time by 30% and minimizing rollback incidents by 25%.

Accelerated enterprise onboarding by leading AI proof of concept to production deployments, reducing customer time to value by 40% and increasing cross team adoption.

Created architecture documentation and delivered technical enablement sessions for engineering teams, reducing integration support requests by 25% and improving correct AI platform adoption. Technologies Used: Java 17, Spring Boot 3.x, Hibernate 6, AWS (ECS, Lambda, RDS, S3, SQS, CloudWatch, KMS, CloudFormation, CodePipeline, SageMaker), GenAI, LLM, RAG, LangChain, Angular 15, React 18, Python 3.10, Node.js 18, C# (.NET Framework 4.8), PostgreSQL 14, Docker, Jenkins, Kafka, OAuth2, JWT, Terraform 1.4, JUnit 5, Mockito, JProfiler, VisualVM, SonarQube, REST, Microservices, CI/CD, Resilience4j Tenet Healthcare – Dallas, TX February 2021 – May 2023 Software Engineer

Developed scalable backend services using Java 11 and Spring Boot to support patient management systems across 50+ healthcare facilities.

Improved data interoperability and claims accuracy by building secure RESTful APIs, reducing claim rejections by 25%.

Engineered multi-threaded Spring Batch jobs for lab results and clinical data, significantly improving throughput and processing reliability.

Ensured continuity for mission-critical medical systems by deploying services on AWS EC2, S3, and RDS for disaster recovery readiness.

Implemented role-based access control (RBAC) in patient record systems to ensure HIPAA-compliant access to sensitive health data.

Designed event-driven, serverless workflows using AWS Lambda, reducing infrastructure costs and improving execution efficiency.

Enhanced system decoupling and fault tolerance by implementing asynchronous messaging with AWS SQS and SNS.

Streamlined clinician workflows by integrating Angular front-end applications with backend Java APIs for seamless user experiences.

Refactored legacy monoliths into Spring Boot microservices and containerized services with Docker to improve maintainability.

Met HIPAA and HL7 standards by implementing AES-256 encryption for data at rest and in transit.

Enabled reliable EHR integrations by designing custom middleware for HL7 message parsing and transformation.

Reduced query latency by up to 40% by optimizing SQL queries and indexing in PostgreSQL and MySQL.

Achieved 85% code coverage by implementing unit and integration tests with JUnit and Mockito to reduce production defects.

Automated time-sensitive patient communication by designing an alerting system using AWS SNS and Lambda.

Enabled faster and safer releases by automating CI/CD pipelines with Jenkins and AWS CodeDeploy for zero-downtime deployments.

Integrated AI-driven risk detection services into healthcare platforms to support data-informed clinical decision-making.

Technologies Used: Java 11, Spring Boot 2.x, Hibernate, Angular 12, RESTful APIs, AWS (EC2, S3, RDS, DynamoDB, Lambda, SQS, SNS, CodeDeploy, CloudWatch), JUnit 5, Mockito, Git, Jenkins, Maven, Docker, PostgreSQL, MySQL, SQL, Python 3.x, C# (.NET Framework), HL7, HIPAA, Spring Batch, AES Encryption, Agile (Scrum), Microservices, IntelliJ IDEA, Eclipse Walmart –Sunnyvale, CA July 2019 – January 2021

Java Developer

Engineered full stack services using Java, Spring Boot, Angular, Node.js, and Python supporting high traffic retail and internal platforms, increasing feature delivery speed by 30% across business teams.

Redesigned and exposed RESTful APIs and backend microservices consumed by multiple downstream teams, reducing integration defects by 25% and strengthening cross team interoperability.

Built high throughput Kafka based event pipelines processing millions of asynchronous retail events per day, improving real time inventory and order synchronization accuracy by 35%.

Rearchitected message driven services to reduce system coupling, increasing platform scalability and lowering production incident frequency by 20%.

Reduced customer facing disruptions by 25% by introducing retries, timeout controls, and graceful degradation patterns across distributed services.

Aligned backend services with Walmart Solution Security Plan compliance standards, contributing to successful internal audits and zero critical security violations.

Streamlined CI CD pipelines with automated testing and deployment workflows, decreasing release cycle time by 30% and improving deployment reliability.

Strengthened application stability by writing comprehensive unit and integration tests, lowering post release defects by 30%.

Resolved production incidents during on call rotations, decreasing mean time to recovery by 20% and improving overall platform reliability.

Containerized microservices using Docker across AWS environments, improving environment consistency, and reducing configuration related deployment issues by 25%.

Leveraged Node.js and Python to automate retail data processing workflows, cutting manual operational effort by 35% and accelerating internal reporting turnaround.

Optimized backend data retrieval across distributed databases, reducing query latency and improving high traffic transaction response times by 20%.

Maintained backward compatibility for legacy integrations while transitioning services to modern Spring Boot microservices architecture, minimizing disruption to dependent systems. Technologies Used: Java 8/11, Spring Boot, Angular, Node.js, Python, Kafka, AWS, Docker, Jenkins, REST APIs, CI/CD

Education

Jacksonville State University

Bachelor of Science in Computer and Information Sciences GPA: 3.92 / 4.00

Certifications

AWS Certified Machine Learning – Specialty

AWS Certified Solutions Architect – Associate

Oracle Certified Professional, Java SE

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