Open to senior backend roles

Ambuj Rajput

Senior Backend Engineer

10+ years building production-grade Java systems at Mastercard, Sopra Banking, and high-growth startups. Expert in Spring Boot microservices, Apache Kafka event-driven architectures, and distributed systems that scale. Pioneering AI-assisted engineering with Spring AI, RAG, and MCP.

10+ Years Java BackendSpring Boot & KafkaMicroservices ArchitectureSpring AI & RAGFintech · Banking · Gaming
View ExperienceResume

About

Architecting systems that scale and endure

I'm a Senior Backend Engineer with over 10 years of experience designing and building distributed systems for enterprise-scale environments. My career spans three demanding domains — fintech, banking, and gaming — each with its own constraints around correctness, latency, compliance, and scale.

At Mastercard, I architect event-driven microservices processing 100K+ daily requests with 99.9% uptime, using Kafka as the integration backbone and Spring Batch for high-throughput data pipelines. Before that, I led backend development for a high-availability online casino platform and modernized batch processing infrastructure at Sopra Banking Software.

I'm actively pioneering AI-assisted engineering — leading adoption of Spring AI and Retrieval-Augmented Generation (RAG) for intelligent log analysis via MCP integrations, cutting debugging time by 30%. I've introduced GitHub Copilot Enterprise workflows that boosted team productivity by 25%.

I care deeply about observability — a system isn't production-ready until you can understand its failure modes. I've built Splunk dashboards, Dynatrace monitors, and Micrometer-instrumented metrics that have directly reduced MTTR by 25% in production incidents. I also mentor engineers and treat code review as a teaching moment.

Fintech & PaymentsBanking & LendingiGaming & CasinoAI-Assisted Engineering

Experience

10+ Years

Java Backend Engineering

Location

O'Fallon, MO

Open to remote roles

Education

B.Tech IT

UP Technical University · CDAC

Focus

Distributed Systems + AI

Kafka · Spring AI · RAG · MCP

13+

Years Java

3

Industries

95%+

Test Coverage

Skills

Technical Depth

13+ years in the Java ecosystem — from RESTful APIs to distributed event-driven systems.

Languages & Frameworks

Core Java ecosystem

  • Java13y
  • Spring Boot10y
  • Spring Batch8y
  • Spring Security7y
  • Spring Cloud6y
  • Spring WebFlux4y
  • gRPC / Protobuf3y

Architecture & Patterns

System design expertise

  • Microservices8y
  • Event-Driven Architecture7y
  • Distributed Systems8y
  • CQRS4y
  • API Gateway Patterns5y
  • Domain-Driven Design3y
  • RESTful API Design10y

Messaging & Data

Event streaming and persistence

  • Apache Kafka8y
  • IBM MQ4y
  • Spring Cloud Stream4y
  • PostgreSQL10y
  • Oracle DB5y
  • MongoDB5y
  • Redis6y
  • Memcached3y
  • MySQL4y

Cloud & DevOps

Infrastructure and delivery

  • AWS (EC2, S3, Lambda, RDS)6y
  • Pivotal Cloud Foundry (PCF)6y
  • Docker6y
  • Kubernetes3y
  • Jenkins8y
  • Maven12y
  • Git / GitHub12y

Observability & Security

Production reliability

  • Splunk7y
  • Dynatrace6y
  • Prometheus / Grafana4y
  • Micrometer4y
  • OAuth 2.0 / JWT7y
  • mTLS4y
  • SonarQube / Checkmarx6y

Testing & Quality

Engineering confidence

  • JUnit 510y
  • Mockito10y
  • RestAssured5y
  • Gatling (Load Testing)3y
  • Spring Boot Test8y
  • Test Containers2y
  • Black Duck2y

AI & Developer Productivity

Intelligent engineering workflows

  • Spring AI1y
  • RAG (Retrieval-Augmented Generation)1y
  • MCP (Model Context Protocol)1y
  • LLM Integration1y
  • Prompt Engineering1y
  • GitHub Copilot Enterprise2y
  • AI-assisted SDLC1y
Proficiency:ExpertAdvancedProficientFamiliar

Experience

Engineering Case Studies

10+ years across fintech, banking, and gaming — each role as a deep technical engagement, not just a job.

Challenge

Legacy customer onboarding systems required manual intervention and couldn't handle the scale of PAN registration across heterogeneous downstream domains. Batch jobs were slow and unreliable, causing SLA breaches for enterprise clients.

Approach

Designed an event-driven microservices architecture using Apache Kafka as the central nervous system for data propagation. Built RESTful APIs backed by Spring Boot with APIGW and Akamai at the edge. Modernized Spring Batch workflows with parallel partitioning and optimized Spring Batch workflows to process millions of transactions efficiently. Introduced Spring AI and RAG for intelligent log analysis.

Impact

  • 20% reduction in batch job execution time across millions of daily transactions
  • 95%+ test coverage via JUnit and Mockito, catching production regressions before release
  • 25% reduction in MTTR through enhanced Splunk, Dynatrace, Micrometer, and Prometheus observability
  • 30% reduction in debugging time via Spring AI and RAG-powered log analysis with MCP integrations
  • 25% improvement in development productivity through GitHub Copilot Enterprise AI-assisted workflows
  • Smooth mainframe-to-cloud migration for existing enterprise customers

Technology Stack

JavaSpring BootSpring BatchSpring AIApache KafkaIBM MQPostgreSQLRedisOAuth 2.0mTLSProtobufgRPCJUnitMockitoMavenPCFAWSSplunkDynatraceMicrometerPrometheusCheckmarxGitJenkins
View full experience history

Projects

Architecture-First Engineering

Production systems designed for scale, reliability, and operational clarity.

productionEvent-Driven

Account Catalog Interface

Universal Specification Processor — Mastercard

A production-scale event-driven microservices platform enabling seamless PAN registration across diverse customer systems at Mastercard. Processes 100K+ daily requests with 99.9% uptime through Kafka-backed pipelines with strong consistency guarantees.

Architecture

  • RESTful ingestion APIs secured by OAuth 2.0 scopes behind Akamai CDN + APIGW, processing 100K+ daily requests
  • Kafka as the event backbone: each registration event published to a topic consumed by domain aggregators
  • Spring Batch with partitioned steps for high-throughput bulk processing (millions of records/run)
  • CQRS pattern: write APIs → Kafka → read-optimized projections in PostgreSQL

Outcomes

  • 20% reduction in batch execution time processing millions of daily transactions
  • 95%+ test coverage with JUnit and Mockito, near-zero production regressions
  • 25% reduction in MTTR through enhanced Splunk, Dynatrace, Micrometer, and Prometheus observability
JavaSpring BootSpring BatchSpring AIApache KafkaIBM MQPostgreSQL+11
productionMicroservices

Remote Gaming Service

Casino Platform Backend — Ingenuity Gaming

A high-availability microservices backend powering live online slot and table games for a regulated casino platform. Architected to sustain 1,000+ concurrent sessions with 99.9% uptime and sub-second response times.

Architecture

  • Game-domain microservices: each game type (slots, tables, jackpots) as isolated Spring Boot services
  • Kafka for real-time game event streaming, audit trails, and jackpot computation
  • Redis for player session state — avoids DB round-trips on every game action
  • Keycloak with OpenID Connect for centralized SSO and identity management

Outcomes

  • 1,000+ concurrent users with 99.9% platform uptime achieved
  • 5+ games delivered ahead of client deadlines
  • 20% reduction in incident response time with structured observability
JavaSpring BootApache KafkaMongoDBRedisJWTOAuth 2.0+7
legacyBatch Processing

Lending & Leasing Banking Platform

High-Throughput Batch Processing — Sopra Banking

Backend modules for a core banking lending and leasing product, featuring optimized Spring Batch pipelines that process thousands of daily financial transactions with 100% regulatory compliance.

Architecture

  • Spring Batch with optimized chunk-oriented processing for transaction workflows
  • Kafka for real-time event streaming and integration with external banking systems
  • Oracle DB with tuned queries for high-volume financial data processing
  • AWS EC2 for compute + S3 for batch report storage and archival

Outcomes

  • 25% reduction in data processing time for daily financial batches
  • 100% compliance with security and coding standards via automated gates
  • 20% team productivity improvement through structured mentoring
JavaSpring FrameworkSpring BatchApache KafkaOracle DBAWS (EC2, S3)Jenkins+2

System Design

Technical Notes & Architecture Insights

Deep dives on distributed systems, event-driven design, and backend engineering — from 10+ years in production.

FeaturedApache KafkaEvent-Driven Architecture

Event-Driven Microservices with Apache Kafka

Patterns, Pitfalls, and Production Lessons

A deep-dive into building resilient event-driven architectures with Kafka — covering topic design, consumer group strategies, exactly-once semantics, and hard-won lessons from fintech production systems.

12 min read·Nov 2024Read

Contact

Let's talk systems and opportunities

Open to senior backend roles, principal engineer positions, and consulting in Java, Spring Boot, and distributed systems.

O'Fallon, MO · Remote-friendly

Email

ambraj@gmail.com

Best for detailed inquiries

LinkedIn

linkedin.com/in/ambraj

Connect professionally

GitHub

github.com/ambraj

Code and contributions

Resume

Download PDF

Full work history