AI Engineer with 4+ years in Java, Spring Boot, Microservices & Cloud.
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Software Developer with 4.5 years of experience in designing and developing scalable, production-grade backend systems using Java (8/17), Spring Boot, Python, Fast API, Microservices, and REST APIs — deployed on Kubernetes, OpenShift, and IBM Cloud. Strong foundation in database design with MySQL, PostgreSQL. Experienced with event-driven distributed systems using Apache Pulsar and asynchronous triggers. At IBM, extended this expertise into Agentic AI — built and deployed multi-agent system development using AutoGen, MCP, A2A protocols, and LLM workflows on IBM WatsonX, reducing manual QA effort by 40–60%.
Nirma University, Ahmedabad
B.Tech · Information Technology
August 1, 2017 – June 30, 2021
IBM
Application Developer | Backend · Cloud · Agentic AI
July 1, 2024 – Present
India
Cognizant
Software Engineer | Full Stack Developer
August 1, 2021 – April 1, 2024
India
Agentic QES Bee — Multi-Agent NFT Automation Platform
February 1, 2026 – Present
A multi-agent AI system that automates the end-to-end Non-Functional Testing (NFT) lifecycle by orchestrating 6+ specialized AI agents to generate test plans, scenarios, defect reports, and test closure summaries — reducing manual QA effort by 40–60%. Built and Automated end-to-end development of 6+ AI agents and agentic workflows for test-plan generation, test-scenario creation, test-summary creation, and defect tracking, reducing manual Quality Assurance (QA) effort by 40–60% across the Non-Functional Testing (NFT) lifecycle. Designed and implemented the complete orchestration layer enabling seamless multi-agent interaction across the full NFT pipeline, replacing a previously manual process affecting 3+ QA teams. Designed MinIO-based artifact storage architecture for test artifacts and configured Arize Phoenix for distributed tracing and observability, reducing debugging time by ~50% in complex multi-agent flows. Architected event-driven workflows using webhooks for asynchronous agent triggering, enabling decoupled, scalable processing across distributed components. Built and managed agentic systems using AutoGen and Agent Coreverse (AO) frameworks, accelerating agent development cycles by 35% versus manual orchestration approaches. Integrated 5+ external tools and services with agents using MCP (Model Context Protocol) and A2A protocols, improving interoperability and reducing cross-service execution latency.
dSTK — Distributed Software Test Kit (Chaos Engineering Platform)
August 1, 2024 – February 1, 2026
A chaos engineering platform for evaluating microservice resilience across hybrid-cloud environments (Kubernetes/OpenShift) featuring an AI-driven fault recommendation engine and advanced fault-injection library across 22 services and 4 clusters. Designed and delivered 7+ scalable microservices for a distributed chaos testing system running on Kubernetes and OpenShift, enabling fault injection across 22 services and 4 production clusters. Built the Fault-Scheduler service to schedule and inject targeted faults (nodes, pods, services) using Kubernetes CronJobs and Jobs, automating failure simulation that previously required manual intervention. Developed the Discovery Service for secure Kubernetes/OpenShift cluster onboarding, reducing new cluster registration time from hours to minutes. Created the Auto-Sequencer algorithm to sequence faults based on resource family tree hierarchy, improving test accuracy and preventing cascading failure misattribution. Implemented real-time notification service using Server-Sent Events (SSE) and Apache Pulsar producer/consumer architecture, delivering sub-second fault status updates to 10+ stakeholders. Developed Python-based fault injection (chaos attack) service using Kubernetes and Chaos Toolkit APIs, enabling programmatic execution of 15+ attack scenarios. Deployed Apache Pulsar to cloud and built backend services for multi-tenant namespace and topic management, enabling decoupled event streaming across all microservices. Implemented centralized JWT Security Authorization across all microservices, standardizing authentication and reducing security misconfiguration risk. Managed Kubernetes/OpenShift deployment resources (Deployments, Jobs, CronJobs, Pods, Services, Ingress, ConfigMaps, Secrets, Virtual Services, Gateways) for production-grade cloud-native infrastructure.
MetLife Insurance — MetOnline Portal (WebSBR, WebSOH)
August 1, 2021 – April 1, 2024
Enterprise insurance platform enabling brokers, users, and admins to manage flexible benefit plans, apply for life/dental/eye-care insurance, and submit claims with real-time reimbursement tracking. Supported 100,000+ active policy transactions. Developed and maintained 4 enterprise insurance web applications (MetOnline Portal, WebSBR, WebSOH, MetSOH) serving brokers and administrators managing flexible benefit plans and insurance policy lifecycle. Executed Struts 1 to Struts 2 migration and subsequent Struts-to-Spring MVC migration for legacy modules, improving application performance and maintainability across 3 core services. Designed and managed MySQL relational database schema supporting multi-plan insurance data, optimizing query performance for daily premium updates and claims processing. Developed 20+ REST APIs and validated them using Postman, integrating frontend React.js components with backend services for real-time claims submission and reimbursement tracking. Implemented frontend modules using React.js, JSP, and Thymeleaf for dynamic user interfaces serving claims upload, document management, and policy status tracking. Resolved 30+ security vulnerabilities identified through Veracode static analysis, improving application security posture and ensuring compliance with enterprise-grade standards.
AWS Certified Cloud Practitioner
AWS
June 1, 2026 – Present
Java
Udemy
June 1, 2026 – Present
Python
Udemy
June 1, 2026 – Present
Kubernetes
Udemy
June 1, 2026 – Present
Agentic AI
Udemy
June 1, 2026 – Present
Generative AI (GenAI)
Udemy
June 1, 2026 – Present
Google Kickstart Competitive Programming
January 1, 2020 – Present
Cultural Fit Analysis
The candidate demonstrates a strong cultural fit for an innovative and technically demanding environment. Their work on multi-agent AI systems and chaos engineering platforms shows a proactive approach to leveraging cutting-edge technologies and solving complex, real-world problems. The breadth of technologies used (Java, Python, Spring Boot, Kubernetes, OpenShift, various AI frameworks, databases) indicates a continuous learning mindset and adaptability. The detailed project descriptions, especially the quantifiable impact (e.g., reducing QA effort, debugging time), suggest a focus on delivering tangible value and a strong sense of ownership. Their experience across different domains (NFT automation, chaos engineering, enterprise insurance) also points to versatility and a willingness to tackle diverse challenges.
Soft Skills & Operational Fit
The candidate's project descriptions highlight strong problem-solving skills, particularly in automating complex workflows and improving system resilience. Their experience in orchestrating multiple AI agents and designing event-driven architectures suggests an ability to manage complex, interdependent systems. The focus on reducing manual effort and improving efficiency (e.g., 40-60% QA effort reduction, 50% debugging time reduction) indicates a results-oriented approach. The detailed descriptions of designing and implementing various services and algorithms also point to strong analytical and design capabilities. The candidate's experience with diverse technologies and project types suggests adaptability and a proactive learning attitude.