
Versatile and results-driven software engineering leader with 17+ years of experience designing and delivering distributed microservices, event-driven platforms, and large-scale data systems across Java, Python, and C# ecosystems. Proven expertise in architecting cloud-native applications on AWS and Azure, building high-performance RESTful and GraphQL APIs, and implementing real-time streaming architectures with Apache Kafka.
Extensive experience designing scalable platforms using Kubernetes, Docker, and Terraform, while working with modern data stores including MongoDB, Redis, DynamoDB, and OpenSearch/ElasticSearch to process and analyze large-scale datasets.
Integrate AI capabilities into production systems, leveraging tools like GitHub Copilot, Claude Code, and Windsurf to accelerate delivery and improve developer productivity. Currently exploring Agentic AI architectures and implementing Model Context Protocol (MCP) while continuing to build scalable event-driven systems on AWS.
A collaborative leader passionate about mentoring teams, improving developer workflows, and delivering reliable, high-impact distributed systems at scale
Languages & Frameworks: Java (8–17), C# / NET Core, Spring Boot, Spring MVC, Spring Batch, Spring AI, REST, GraphQL, OpenAPI Specification, SwaggerHub
Cloud & Infrastructure: EC2, EKS, S3, Amazon EventBridge, SQS, Lambda, DynamoDB, Redshift, Aurora, API Gateway, Cognito, IAM,Azure (Blob Storage, Cosmos DB, Key Vault) Docker, Kubernetes, Helm, Terraform, Terragrunt,GitHub Actions, Jenkins
Testing Frameworks: TestNG,JUnit,Mockito,WireMock,REST Assured
Source Code Version Control: Git,Bitbucket,SVN
Containerized Tools: Kubernetes, Docker
Databases: Oracle,MySQL,Postgres SQL,Apache Cassandra, MongoDB,Azure
Data, Streaming & Search: Apache Kafka, Apache Flink, Apache Spark, Amazon Redshift OpenSearch / Elasticsearch, DynamoDB, Cassandra, MongoDB, Vector Databases
AI & Automation: Spring AI, OpenAI APIs (summarization, classification, enrichment) Vector embedding pipelines , N8N automation workflows
AI & Agentic Systems:
- Spring AI, OpenAI APIs (summarization, classification, enrichment)
- Vector embedding pipelines and semantic search
- Model Context Protocol (MCP) server implementation and tool exposure
- Agentic AI architecture and agent-to-agent (A2A) communication patterns
- Building interoperable AI agents with handoff and orchestration strategies
- GitHub Copilot, Claude Code (Anthropic) for AI-assisted code generation, refactoring, debugging, test case generation, and documentation acceleration