Senior Golang Engineer - Generative AI Platform

We are seeking a Senior Golang Engineer to join a growing team focused on building enterprise-scale Generative AI platforms and developer tooling. This is an opportunity to work at the intersection of cloud-native engineering, distributed systems, and AI, helping deliver the next generation of AI-enabled capabilities within a global financial services environment. We're looking for strong T-shaped engineers with deep expertise in Golang and broad engineering capability across Kubernetes, platform engineering, DevOps, scalability, and distributed systems. Commercial AI experience is not required, but a genuine interest in Generative AI and emerging technologies is essential.

Responsibilities

• Design, develop and operate scalable backend services using Golang.
• Build cloud-native platforms supporting Generative AI and LLM-powered applications.
• Develop reusable libraries, frameworks and internal developer tooling.
• Design and implement distributed systems with a focus on reliability, performance and scalability.
• Collaborate with platform, infrastructure and AI engineering teams.
• Drive engineering best practices, observability, automation and operational excellence.
• Mentor engineers and contribute to technical leadership across the team.

Qualifications

• Strong commercial experience developing software in Golang.
• Deep understanding of distributed systems and backend architecture.
• Strong Kubernetes knowledge and experience running applications in production.
• Experience with microservices, APIs, event-driven architectures and cloud-native technologies.
• Strong understanding of CI/CD, DevOps and platform engineering principles.
• Experience building highly scalable, resilient systems.
• Excellent communication and stakeholder management skills.

Candidates should possess a strong understanding of Kubernetes concepts equivalent to the core topics covered in Chapters 1 6 of: Kubernetes in Action Including:

• Pods, Deployments and ReplicaSets
• Services and Networking
• ConfigMaps and Secrets
• Resource Management
• Health Checks and Application Lifecycle
• Namespaces and Workload Management
• Kubernetes Architecture and Operational Concepts

Commercial AI experience is not required. However, candidates should demonstrate:

• Strong interest in Generative AI and LLM technologies.
• Understanding of modern AI tooling and frameworks.
• Experience experimenting with AI-assisted development tools.
• Curiosity around agentic systems, RAG architectures, and enterprise AI adoption.

Desirable Skills

• Experience with AI/ML platforms or developer productivity tooling.
• AWS, Azure or GCP experience.
• Experience with Kafka, messaging systems or event-driven architectures.
• Financial services or regulated industry experience.
• Platform Engineering or Internal Developer Platform experience.