Node JS and Java Full Stack Engineer

Key Responsibilities

Design and develop end-to-end features across the React front-end and Java/Spring Boot or Node.js back-end microservices.

Build rich model monitoring dashboards that visualize latency, throughput, drift, accuracy, and model health metrics using React, TypeScript, and charting libraries (D3.js, Recharts, Plotly).

Collaborate with the MLOps team to integrate Seldon APIs for model deployment, inference routing, version control, and real-time feedback loops.

Build secure, scalable REST/gRPC APIs for managing model metadata, performance telemetry, and inference results.

Implement automated testing and CI/CD workflows to ensure safe, frequent deployments.

Integrate with Prometheus/Grafana or OpenTelemetry for live observability of model performance and microservice health.

Work closely with architects and DevOps to ensure Kubernetes-native deployment and scaling across cloud environments (AWS EKS / GCP GKE / Azure AKS).

Contribute to design discussions, code reviews, and platform documentation.

Required Qualifications

8-12 years of full-stack software development experience in enterprise-scale distributed systems.

Strong back-end expertise in Java (Spring Boot) or Node.js/Express, with exposure to microservices, message queues (Kafka), and asynchronous processing.

Front-end proficiency with JavaScript/TypeScript, React, Redux, HTML5, CSS3, and RESTful integrations.

Experience working with data visualization frameworks and dashboards for analytics or ML monitoring.

Familiarity with Seldon, KServe, TensorFlow Serving, or other model serving frameworks.

Hands-on experience with Docker, Kubernetes, and cloud services (AWS, GCP, or Azure).

Understanding of CI/CD pipelines, Git, testing frameworks (JUnit, Jest, Cypress), and agile methodologies.

Knowledge of API security, JWT/OAuth2, and role-based access control (RBAC).

Preferred Skills

Familiarity with MLOps workflows and model lifecycle management (build deploy monitor retrain).

Experience building micro-frontend architectures and integrating with service mesh (Istio/Envoy) for cross-service communication.

Exposure to observability stacks (ELK, Grafana Loki) and log/metric aggregation.

Working knowledge of streaming frameworks (Kafka, Flink) for real-time inference monitoring.

Strong UI/UX sensibility, especially around data visualization and operational dashboards.