What You'll Do:

• Provide technical leadership across multiple teams and backend systems, owning the architecture of high-throughput, low-latency services built primarily on Java and Spring Boot.
• Drive architecture decisions for distributed systems, including service boundaries, data modelling, storage choices, and scalability, so the platform stays future-proof as volume and complexity grow.
• Tackle the most complex architectural challenges in your area, and partner with Tech Leads and Engineering Managers to shape the technical vision and roadmap.
• Lead critical cross-team technical initiatives end to end, from design through rollout, aligning engineering work with product and business goals.
• Act as the technical lead for your product line, breaking down complex problems into well-defined work, providing clear technical guidance, and unblocking engineers.
• Mentor senior and junior engineers through design reviews, code reviews, and pair programming, raising the technical bar and fostering a culture of ownership and continuous learning.
• Design and build systems that make effective use of Redis across a range of patterns, such as caching, real-time counters, rate limiting, distributed locking, pub/sub, and streams, alongside other datastores.
• Build and evolve event-driven and asynchronous systems using messaging and streaming platforms such as Kafka.
• Lead the integration of AI into our products and engineering workflows, including LLM-powered features, agentic workflows, retrieval-augmented generation (RAG), and tool integrations via the Model Context Protocol (MCP).
• Champion AI-assisted software development across the team, setting standards for how engineers use coding agents and AI tools responsibly to improve speed and quality.
• Establish and enforce engineering best practices around API design, code quality, testing, security, and data privacy, with healthcare compliance expectations in mind.
• Design observability strategies (metrics, logging, tracing, alerting) using tools such as Grafana and Prometheus to proactively identify bottlenecks and ensure production stability.
• Lead deep-dive investigations into complex production issues and drive root-cause fixes and blameless postmortems.
• Drive knowledge sharing across engineering through technical documentation, design forums, and internal talks.

Who You Are:

• 8+ years of backend engineering experience, with a track record of designing and delivering large-scale distributed systems in production.
• Deep expertise in Java (17+) and the Spring ecosystem (Spring Boot, Spring Cloud, Spring Data), including a solid understanding of JVM internals, concurrency, and performance tuning.
• Strong working knowledge of Redis, including its data structures, persistence and clustering models, and how to apply it to problems beyond caching, with a clear understanding of its trade-offs at scale.
• Solid experience with relational and NoSQL databases (e.g., MySQL, PostgreSQL, Aerospike, Cassandra or similar), including query optimisation and schema design.
• Experience with messaging and streaming systems such as Kafka, and with designing resilient, idempotent, event-driven architectures.
• Strong grounding in microservices, REST/gRPC API design, and distributed systems fundamentals (consistency, partitioning, fault tolerance).
• Experience with cloud platforms (GCP or AWS), containerisation (Docker, Kubernetes), and owning CI/CD pipelines.
• Working knowledge of current AI trends and their practical engineering application: LLM APIs, prompt and context engineering, agentic systems, RAG and vector databases, MCP, and evaluation of AI system quality and reliability.
• Hands-on experience using AI coding tools (such as Claude Code, GitHub Copilot, or Cursor) in day-to-day development, with a clear view of where they help and where human judgment is essential.
• Proven ability to lead technical work across multiple teams, influence architectural decisions without direct authority, and mentor senior engineers.
• Clear communicator who can explain complex technical trade-offs to both technical and non-technical stakeholders.
• Experience in ad tech (programmatic advertising, RTB, DSP/SSP systems) or healthcare data platforms is a plus.
• Experience building production LLM features, including guardrails, cost and latency optimisation, and observability for AI workloads, is a plus.
• Familiarity with big data processing frameworks such as Spark or Flink is a plus.
• Contributions to open-source projects, or experience with technical writing or speaking, are a plus.