Java Developer /AI experience

Leads end-to-end delivery of full stack solutions using Java Spring Boot, GraphQL/REST, and AWS-managed services, from design through production support.

Drives architecture and design reviews, defining service boundaries, API contracts, data models, and integration patterns.

Develops secure, high-quality, production-grade code; conducts thorough code reviews; sets and enforces engineering standards.

Builds and maintains highly available and scalable services with strong operational excellence: reliability, performance, cost awareness, and maintainability.

Implements and governs authentication/authorization using OIDC/OAuth2, token validation, scopes/claims, and service-to-service security patterns.

Establishes robust observability practices (structured logging, metrics, tracing), incident response readiness (runbooks).

Evaluates vendor and internal solutions (including architectural fit, security posture, scalability, and operability) and guides adoption decisions

Minimum 12+ years applied full stack engineering experience delivering and operating production systems; experience as a senior/lead capacity (technical leadership, design ownership).

Java Spring Boot expertise: building APIs, dependency injection, validation, error handling, and performance tuning.

GraphQL proficiency: schema design, resolvers, federation/composition patterns (where applicable), query performance (N+1 mitigation, caching), and API governance/versioning strategy.

REST proficiency: OpenAPI-style contract design, pagination/filtering, idempotency, backward compatibility, and consumer-driven API evolution.

OIDC/OAuth2: hands-on implementation with JWTs, refresh/access token flows, scopes/claims, secure session patterns, and least-privilege access control.

AWS experience (hands-on): designing, deploying, and operating cloud-native applications; strong understanding of IAM, networking basics, and operational tooling (logs/metrics/alarms).

AWS Aurora PostgreSQL: advanced SQL, indexing, query optimization, migrations, and transactional design.

AWS Neptune: gremlin, graph modeling, query patterns, and performance considerations for graph workloads.

Event-driven systems

AWS Kafka (MSK): topics/partitions, consumer groups, schema evolution, ordering/at-least-once semantics, idempotency, retries/DLQs, and operational monitoring.

CI/CD and quality

Strong Git practices, automated testing (unit/integration/contract/e2e), code quality gates, and release confidence practices (feature flags, progressive rollout where applicable).

AI-assisted engineering (MUST):

Demonstrated proficiency using Claude Code and/or GitHub Copilot to accelerate development, debugging, refactoring, test generation, and documentation—while ensuring correctness, security, and maintainability (e.g., validating outputs, avoiding sensitive data leakage, and adhering to team standards).