Senior Platform Engineer - Real-Time Data & ML

We're seeking an experienced Platform Engineer to help develop the infrastructure behind sophisticated, Real Time data and machine learning workloads.

You'll be responsible for solving challenging engineering problems across distributed systems, streaming data and cloud infrastructure. The role will suit someone who enjoys working at scale, takes ownership of complex technical challenges and can build platforms that are both highly performant and dependable.

The Role:

• Take end-to-end ownership of the infrastructure supporting Real Time machine learning and data-driven applications.

• Develop highly scalable systems capable of handling significant volumes of continuously generated data while maintaining consistently fast response times.

• Create platform components, tooling and engineering standards that make it easier for development teams to deliver and run reliable services.

• Build and maintain event-driven architectures and Real Time data pipelines designed for demanding, latency-sensitive workloads.

• Identify opportunities to improve system performance, resilience, scalability and operational efficiency.

• Collaborate with engineers and specialists across machine learning, data, Back End and product to understand technical challenges and deliver effective solutions.

• Contribute to architectural decisions and help shape the future direction of the platform.

• Act as a technical role model within the team, supporting colleagues through mentoring, knowledge sharing and constructive engineering practices.

Essential Skills:

• Professional experience in platform, infrastructure, systems or software engineering, ideally within complex production environments.

• Strong practical knowledge of Kubernetes and experience running services reliably in containerised environments.

• A good grasp of distributed computing principles, networking, system performance, fault tolerance and designing for high availability.

• Experience working with systems where latency and throughput are important considerations.

• The ability to assess different technical approaches and make sensible decisions around scalability, reliability, performance and infrastructure costs.

• Confidence operating across different technical areas, including cloud infrastructure, Back End services, data platforms and machine learning systems.

• A pragmatic approach to engineering, with the ability to take loosely defined problems and turn them into clear, maintainable solutions.

• Strong communication skills and the ability to work effectively with both technical and non-technical stakeholders.

• A high degree of autonomy, curiosity and ownership, alongside a willingness to support the development of other engineers.

Desired Skills:

It would be advantageous if you have worked with technologies such as Kafka, Flink or ClickHouse, or similar tools used for event streaming, Real Time processing and large-scale analytical workloads.

Experience building platforms for low-latency applications, Real Time ML or high-volume data environments would also be highly relevant.