Staff Software Engineer- Eng

AI-Augmented Development - Use AI coding tools for development, refactoring, debugging, testing, and documentation. Validate all AI-generated output, own what ships, and model responsible AI-assisted engineering practices. Full Stack Engineering - Build and deliver production-grade features across the stack using Java, Spring Boot, Angular, and TypeScript, with strong API design, security, scalability, and end-to-end ownership. Micro Frontend Architecture - Design and implement micro frontend architectures that reduce coupling, support independent deployment, and fit the product's actual needs. Agentic Workflow Implementation - Build agentic AI workflows with clear task boundaries, handoffs, and quality gates, and apply them where they add meaningful value. LLM Integration - Integrate LLM APIs into product features and internal tools using structured outputs, tool use, and the right mix of prompting, RAG, or fine-tuning. Prompt Engineering & Context Management - Create structured prompts, manage context carefully, improve output quality systematically, and balance reliability with token efficiency. Spec-Driven Development - Write clear specifications before coding, use AI to strengthen them, and reduce ambiguity and rework. Quality Gates & Engineering Excellence - Maintain high quality standards, validate AI-generated work, and drive strong engineering and testing practices. AI-Assisted Code Review - Use AI to strengthen code reviews, identify risks, and provide clear feedback while ensuring compliance with team standards and security expectations. AI-Assisted Testing - Build testable software, use AI to improve test coverage and edge-case detection, and ensure test quality across the stack. Service Health & Observability - Build strong observability into services, monitor proactively, and drive root-cause fixes for production issues. DevOps Ownership - Own the full service lifecycle, including delivery, deployment, CI/CD, containerisation, and production operations. Leadership & Mentorship - Lead technical decisions, coordinate delivery, mentor engineers, and promote a culture of learning, ownership, and excellence. Documentation - Create and maintain clear technical documentation, using AI to accelerate drafting while ensuring human-reviewed accuracy. ________________________________________ ================================================================================ ================================================================================ - Bachelor's or Master's degree in Computer Science, Engineering, or a related technical field, or equivalent experience demonstrated through a portfolio of delivered work. - 7+ years of professional software development experience. - Java and Spring Boot -- RESTful API design, microservices architecture, and Spring Security for production-grade service security. - Angular and TypeScript -- component architecture, RxJS, state management, and integration with enterprise component libraries. - Micro Frontend Architecture -- practical implementation experience with Module Federation, Single-SPA, or equivalent MFE patterns. - REST APIs: mandatory. GraphQL: strong advantage. - OIDC / JWT-based authentication and authorisation. - Internal service communication -- Service Discovery and routing patterns (e.g., Eureka, Consul, Spring Cloud Gateway). - Distributed caching -- hands-on experience with at least one of: Redis, Memcached, or Hazelcast. - Kafka or equivalent event-streaming platform -- producer/consumer patterns and event-driven architecture fundamentals. - Relational databases: SQL Server or PostgreSQL -- schema design and query optimisation. - Non-relational stores: MongoDB -- data modelling and aggregation. - Build tooling: Maven or Gradle. - Backend testing: JUnit, Mockito, WireMock. - Frontend testing: Jasmine / Jest and Cypress. - Docker and Kubernetes. - CI/CD pipeline ownership. - Cloud platforms: Azure, AWS, or GCP. - Observability: Datadog or Prometheus/Grafana -- structured logging, distributed tracing, metrics, and alerting. ________________________________________ - Consumer-driven contract testing -- Pact or Spring Cloud Contract. - Hazelcast distributed computing beyond caching -- IMDG and distributed computation patterns. - Familiarity with vector databases (Pinecone, Weaviate, pgvector) for RAG pipeline implementation. - Exposure to AI observability -- monitoring LLM latency, token consumption, and output quality drift in production. - Understanding of AI security fundamentals -- prompt injection risks and context leakage mitigations. - Accessibility standards in Angular applications -- WCAG compliance and ARIA patterns. - Commitment to diversity, equity, and inclusion in team building and technical decision-making.