Senior AI/ML Platform Reliability Engineer

J.P. MORGAN

Scotland

On-site

GBP 70,000 - 100,000

Full time

13 days ago

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Job summary

JPMorganChase is seeking a Software Engineer III to join the AI/ML Data Platforms Reliability Engineering team. You will design and implement scalable, secure AI/ML platforms, own NFRs and observability tooling, and partner across teams to deliver high-impact AI use cases.

The role requires strong Python, SDLC, and cloud-native skills, with experience in Terraform, Kubernetes, CI/CD, and advanced observability tools.

Qualifications

Formal training or certification on software engineering concepts.
Hands-on practical experience delivering system design, application development, testing, and operational stability.
Advanced proficiency in Python.
Proficiency in all aspects of the Software Development Life Cycle.
Experience with infrastructure-as-code and cloud-native delivery practices (Terraform, containers, Kubernetes, CI/CD) and automated deployment workflows.
Experience in designing and developing large-scale distributed systems and cloud-native architectures.
Experience building large-scale infrastructure on Google Cloud, AWS, or Azure and Terraform.
Extensive experience implementing observability using OpenTelemetry, Dynatrace, Grafana or similar tools.
Experience using enterprise-authorized AI-assisted development tools with validation of AI outputs.
Understanding of responsible AI use in engineering workflows.

Responsibilities

Design and implement solutions to enhance reliability and scalability of AI/ML platforms.
Own non-functional requirements and develop tooling for observability, security, resilience, and operations excellence.
Build and maintain scalable infrastructure to support deployment and operation of large-scale AI platforms.
Foster cross-functional relationships to deliver user-focused AI solutions.
Participate in on-call rotations and troubleshoot production issues with ownership of resolution.
Develop secure, high-quality production code and review others' code.
Identify opportunities to automate remediation and improve operational stability.
Leverage AI-assisted tooling to improve code quality and delivery speed with peer review and secure coding.