Engineering Manager - Backend Engineering & AI

Responsibilities:

• Lead and develop an engineering team responsible for backend services, APIs, distributed processing and event-driven workflows supporting FMP.
• Own end-to-end delivery and production outcomes, including architecture, implementation, automated testing, deployment, reliability and ongoing improvement.
• Drive sound architectural decisions across microservices, asynchronous processing, messaging, data persistence, API design and service integration.
• Ensure business-critical workflows are correct, resilient, auditable and secure, with appropriate controls for idempotency, retries, duplicate processing, ordering, recovery and data consistency.
• Design for high availability, scalability, latency, throughput and predictable performance under variable workloads.
• Lead the pragmatic adoption of AI-enabled capabilities, including LLM integrations, intelligent automation and agentic workflows where they deliver demonstrable business or engineering value.
• Establish engineering standards for code quality, CI/CD, automated testing, infrastructure as code, observability, security and operational readiness.
• Own production reliability and continuous improvement through incident management, root cause analysis, performance optimisation, resilience engineering and reduction of recurring operational issues.
• Optimise infrastructure utilisation and cloud costs without compromising business-critical SLAs or reliability.
• Partner with Product and Architecture to translate business requirements into maintainable technical solutions, balancing near-term delivery with long-term engineering health.
• Reuse UDP data products, contracts and shared platform capabilities where appropriate, avoiding unnecessary duplication of platform functionality within FMP.
• Build a strong engineering culture based on technical ownership, accountability, continuous improvement and effective collaboration across distributed teams.
• Develop engineers and technical leads, strengthen succession and team capability, and establish predictable delivery practices.

Requirements:

• Typically 10-14 years of overall software engineering experience, including substantial experience in backend engineering and distributed systems; demonstrated engineering leadership is essential.
• Strong experience with Java and/or Python, microservices, REST APIs and distributed application architectures.
• Deep understanding of concurrency, asynchronous processing, messaging, caching, fault tolerance, consistency, high availability and resilience patterns.
• Strong experience with AWS and cloud-native services, including appropriate compute, storage, messaging, container and managed database services.
• Practical experience with event-driven architecture and Kafka or equivalent messaging platforms, including schema evolution, delivery semantics, retries, replay and failure recovery.
• Strong knowledge of relational and/or NoSQL databases, data modelling, query optimisation and high-performance data access.
• Experience with system design, capacity planning, performance profiling, load testing and cost-performance optimisation.
• Hands-on understanding of CI/CD, automated testing, deployment automation, infrastructure as code, security controls and production observability.
• Experience operating production services, defining service-level indicators/objectives, managing incidents and driving root-cause remediation.
• Practical exposure to AI-enabled applications, LLM integration or workflow automation; familiarity with agentic patterns and model-serving platforms is beneficial. AWS Bedrock or Vertex AI experience is an advantage, not a substitute for strong backend engineering.
• Understanding of data contracts, schema governance and integration with analytical data platforms such as BigQuery or Spark is beneficial.
• Familiarity with domain-driven design, API lifecycle management, platform engineering principles and AI-assisted software development practices.

Leadership and Collaboration:

• Proven ability to lead, coach and grow engineering teams, establish clear accountability and improve engineering effectiveness.
• Strong architectural judgment, with the ability to challenge design proposals and balance delivery speed, maintainability, reliability, security and cost.
• Accountability for delivery commitments, engineering quality, production performance and continuous improvement.
• Experience working with Product, Architecture, Data Engineering, Security, Platform Engineering and Operations teams.
• Ability to manage dependencies and influence stakeholders across distributed teams and organisational boundaries.
• Strong communication skills, including the ability to explain technical trade-offs, risks and outcomes to senior leadership.
• Demonstrated ability to develop technical leaders, delegate effectively and build a team that can operate without excessive dependence on individual experts.
• A product and platform mindset focused on reusable capabilities, measurable outcomes and continuous improvement.

Skills

AWS, Java, Microservices, Python