AI-Enabled Software Engineering & Microservices

In this role, you will:

• Design, develop, test, and support microservices using Spring Boot, Java and Python, applying clean architecture and SOLID principles.
• Developer experience uplift: Reduce engineering friction via developer tooling, CI/CD optimization, test automation, observability standards, and self-service enablement.
• Write high-quality automated tests (unit/integration/contract) and contribute to non-functional requirements (NFRs) including resilience, scalability, and observability.
• Collaborate with product, architecture, platform, security, and operations to deliver outcomes at pace. Architecture and technical governance: Lead design reviews decisions.
• Identify and drive AI opportunities across the SDLC (e.g., test generation, refactoring assistance, defect triage, knowledge management, runbook improvements) and/or product capabilities.
• Set AI-enabled engineering direction (platform + product): Define and champion practical AI-enabled patterns (including agent-based tooling) that improve SDLC.
• Prototype AI-assisted solutions, evaluate outcomes (accuracy, cost, latency), and recommend scalable approaches.
• Embed AI into products responsibly: Partner with product engineering to integrate AI into production with appropriate controls, explainability (where relevant), and operational readiness.
• Coach peers on effective, safe usage patterns and contribute reusable components, templates, and guidelines.

To be successful in this role, you should meet the following requirements:

• Strong hands-on development experience with microservices, Spring Boot, Java, Spark, Kubernetes, event-driven architecture.
• Microservices fundamentals: domain boundaries, versioning, backward compatibility, API design, and asynchronous patterns (queues/events).
• CI/CD and engineering: Git, branching strategy, code reviews, build pipelines, automated testing.
• Observability and runtime support: structured logging, metrics, tracing, dashboards, alerting (tooling varies by platform).
• Good understanding of containerization and deployment concepts (e.g., Docker, Kubernetes/OpenShift - whichever applies in your stack).
• Experience applying LLMs / GenAI in engineering workflows (e.g., accelerating development, test strategy, documentation) with measurable improvements.
• AI-enabled software engineering at scale: Experience applying AI (incl. agent-based/AI tooling) to transform how teams build, test, release, and operate software.
• Production AI delivery with measurable impact: Track record embedding AI into products and/or engineering workflows, demonstrating tangible outcomes.
• Awareness of AI risks (hallucination, data leakage, bias) and practical mitigations.
• Strong problem-solving skills and a bias for delivery ("get it done" without cutting corners).

Skills

Docker, Java, Kubernetes, Microservices, Python, Spark, Spring Boot