Role: Senior Java Architect

Location: Menlo Park, CA (Remote)

Duration: 12 Months contract

Visa: Independent Visa only

Job Description:

The ideal candidate will have deep expertise across Java, Spring Boot, event-driven microservices,

cloud platforms, Kubernetes, modern frontend technologies, and enterprise architecture, along with

hands-on experience integrating Generative AI and intelligent application capabilities into enterprise

platforms.

▪ 12+ years of overall software development experience with strong enterprise Java background.

▪ Java 17/21+, Spring Boot, Spring Cloud, Spring Security – strong hands-on experience required.

▪ Enterprise Microservices + Distributed Systems + REST API architecture.

▪ Apache Kafka / Kafka Streams with event-driven architecture experience.

▪ Debezium CDC and real-time data/event processing experience.

▪ Kubernetes + Docker + Helm with production enterprise deployments.

▪ KEDA for event-driven Kubernetes autoscaling.

▪ Istio or Linkerd service mesh experience.

▪ Argo CD / GitOps for Kubernetes CI/CD deployments.

▪ React.js + TypeScript/JavaScript for modern full-stack development.

▪ Generative AI integration into enterprise Java applications is required.

▪ LangChain4j / Spring AI + RAG + AI Agents experience strongly required.

▪ MCP integration with enterprise Java applications is a key differentiator.

▪ Strong AWS/Azure, OAuth2/OIDC, JWT, PostgreSQL/Oracle, Redis and OpenTelemetry

experience preferred.

Pre-Screening Questions

    Q1: Does the candidate have expertise building event-driven microservices using Java 17/21+, Spring Boot, and Spring Security?

    Q2: Has the candidate implemented real-time data processing pipelines and event-driven architectures using Apache Kafka / Kafka Streams along with Debezium CDC?

    Q3: Has the candidate integrated Generative AI, RAG architecture, or AI Agents into enterprise Java applications using tools like LangChain4j, Spring AI, or Model Context Protocol (MCP)?

    Q4: Does the candidate have hands-on experience deploying enterprise workloads to Kubernetes using Helm, GitOps (Argo CD), service meshes (Istio/Linkerd), and KEDA for autoscaling?

All your information will be kept confidential according to EEO guidelines.