Akshay Khule — CTO

●● https://leetcode.com/AkshayKhule/ ●● https://github.com/Akshay-Khule ●● https://writetokaks.medium.com/ I believe in the intersection of entrepreneurship and technology, and I continuously engage in work that brings both together. With 16+ years of experience designing and engineering enterprise platforms, cloud-native systems, and large-scale data solutions, I specialize in building high-performance, low-latency systems using Java, Spring Boot, and modern cloud/data engineering technologies. My experience spans the full engineering lifecycle — from hands-on development and system tuning to architecture, platform modernization, and leading engineering teams. Along with Java-based microservices, I have delivered data and service workloads using Python, Flask, and related ecosystems. Key Expertise: ● Product Architecture & Development – Designed and developed web apps, distributed systems, microservices, and serverless platforms using Java, J2EE, Spring Boot/Cloud, REST, JPA, Dapr, OAuth2, JWT, and Kafka. Apply system design principles, SOLID, clean architecture, DDD, and API-first patterns to build scalable, maintainable, and secure systems. ● Cloud & DevOps – 4× AWS Certified. Built and deployed microservices using Docker + Kubernetes across AWS and Azure. Architected cloud-native, fault-tolerant systems leveraging serverless services, managed databases, event streaming, and service mesh patterns using SQS/SNS, Event Hub, Kafka, and more. ● Data & Analytics Engineering – Developed real-time and batch data pipelines using Spark, EMR, Hive, Kafka, HDFS, NoSQL, Elasticsearch, and data lake security best practices. Skilled in distributed compute, high-volume ingestion, and stream-based architectures. ● End-to-End Engineering Execution – Capable of independently driving requirements, architecture, development, optimization, and cloud deployment. Strong background in scalable, event-driven, and distributed system patterns. ● GenAI & Intelligent Systems – Actively integrating LLM-driven capabilities into cloud and data platforms to enable AI-assisted analytics, automation, and intelligent SaaS workflows. Tech Stack: Java, J2EE, Spring Boot/Cloud, Spring AI, Microservices, REST, Docker, Kubernetes, OAuth2/JWT, JMS, RDBMS, AWS, Azure Python, Agentic AI, MCP, Flask, Data Engineering, MCP Server, Agentic AI, Spark, Kafka, Cloud Data Engineering, NoSQL, Elasticsearch, Synapse, Databricks

Stackforce AI infers this person is a SaaS Architect with extensive experience in cloud-native and data engineering solutions.

Experience: 17 yrs 2 mos