Lead Java Engineer with 7+ years in Spring Boot & Kubernetes
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Backend Developer with 7 years of experience in designing and developing scalable enterprise applications, cloud-native platforms and backend automation solutions using Java, Python, Spring Boot, Kubernetes and Docker. Strong exposure to REST API development, microservices, CI/CD automation, serverless platforms, database optimization and production support. Experienced in platform engineering, distributed systems, infrastructure automation and AI driven solutions within enterprise environments.
IFET College Of Engineering
Bachelor of Engineering · Computer Science
August 1, 2015 – June 30, 2019
TCS
Backend Developer
December 1, 2018 – Present
India
CLOUD PLATFORM ENGINEERING
January 1, 2025 – June 1, 2026
Automated platform provisioning and deployment solutions for enterprise Kubernetes-based environments. Built and configured Kubernetes-based environments supporting APISIX, Kafka, Fission, RabbitMQ, Redis, PostgreSQL, MongoDB and MySQL deployments. Automated product installation and environment provisioning using shell scripting, reducing manual setup efforts by 40%. Developed Python-based orchestration workflows to execute deployment scripts dynamically based on user-defined configurations and inputs. Eliminated direct CLI dependency, improving deployment efficiency and developer productivity by 30-35%. Configured secure inter-service communication and integrated platform services with distributed microservices environment.
SERVERLESS-AS-A-SERVICE
January 1, 2024 – December 31, 2025
Java-based abstraction layers over OpenFaaS, Knative, and Fission to enable API-driven serverless function deployment within Kubernetes environments. Developed Java-based abstraction layers over OpenFaaS, Knative, and Fission to enable API-driven serverless function deployment within Kubernetes environments. Evaluated and migrated serverless implementations from OpenFaaS to Knative to address platform scalability and operational limitations. Built Fission-based serverless deployment workflows for client-specific platform requirements, integrating PostgreSQL-backed configuration management. Eliminated direct CLI dependency by exposing serverless deployment capabilities through backend APIs, improving developer experience and deployment efficiency. Contributed to reusable and extensible serverless platform components supporting enterprise microservices integration.
AI USECASES
January 1, 2024 – December 31, 2025
AI Agents for Root Cause analysis in Kubernetes (RCA), Voice Based Authentication. Developed an LLM-powered self-healing Kubernetes agent to analyse pod logs, detect runtime failures, and trigger automated remediation actions. Implemented automated recovery workflows including pod restarts, scaling adjustments, and configuration corrections to improve operational resilience. Integrated AI-assisted log analysis for intelligent root cause detection and infrastructure monitoring automation. Developed a voice-based biometric authentication proof of concept using LSTM models with anti-spoofing mechanisms for secure authentication workflows.
WORKFLOW & DEMAND MANAGEMENT TOOL
January 1, 2021 – December 31, 2024
Workflow automation and demand management solutions to improve operational efficiency and reporting capabilities. Designed and optimized RESTful APIs for workflow processing, authentication, dashboards, and reporting modules, improving API response time by 25%. Implemented role-based access control and vendor/contract management modules to enhance security and process transparency. Automated demand creation workflows and batch-based notification processes, reducing manual operational effort by 30%. Improved backend performance and database efficiency through query optimization and scalable architecture enhancements during peak system usage. Supported migration from MySQL to PostgreSQL as part of open-source modernization initiatives.
MACHINE LEARNING AS A SERVICE (MLAAS)
January 1, 2019 – December 31, 2021
Cloud-based ML platform integrating data management, machine learning workflows, predictive analytics, recommendation systems and report generation. Developed RESTful APIs and backend services using Python and MySQL to support machine learning platform operations. Built Data ingestion, validation and processing workflows for structures and unstructured datasets. Implemented recommendation, predictive analytics and sequence forecasting capabilities into enterprise application workflows. Integrated machine learning services into web application workflows while ensuring reliability and security.
Google cloud Generative AI Leader Certification
Google Cloud
June 1, 2026 – Present
Digital Training: AZ-204 Developing Solutions for Microsoft Azure
Microsoft Azure
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's project experience spans platform engineering, serverless solutions, AI/ML, and workflow management, demonstrating a broad technical interest and ability to work across different domains. Their involvement in automating processes and improving efficiency aligns with a results-oriented culture. The continuous learning indicated by certifications (Google Cloud Generative AI, Azure) suggests a proactive approach to skill development, which is a positive cultural indicator. The single company experience might suggest less exposure to diverse organizational cultures, but the varied projects within that company mitigate this concern.
Soft Skills & Operational Fit
The candidate's project descriptions highlight leadership in production deployments, incident resolution, and automation efforts, suggesting strong operational acumen and problem-solving skills. Their work on improving developer productivity and efficiency indicates a focus on practical, impactful solutions. The diverse project portfolio also implies good adaptability and a collaborative approach to complex system integrations.