AI DevOps Engineer

Job Description

Responsibilities 

AI Platform and Enablement (primary focus) 

• Design, deploy, and operate AWS Bedrock as the primary inference platform for sovereign internal and client-facing AI workloads, including model access, integration patterns, throughput management, and quota planning across frontier and open-weight model families. 
• Design, deploy, and operate open-weight LLM hosting on AWS for sovereign and client-facing use cases, including model selection, fine-tuning workflows, and inference optimisation. 
• Own the workflow orchestration and agent automation platform, including hosting, scaling, security, integrations, and enablement of business users building workflows. 
• Own model selection and routing across the AI estate so every workload uses the most cost-effective model that meets the quality bar, including modelling unit economics across managed and self-hosted inference options. 
• Stand up and operate enterprise search, retrieval, and embedding infrastructure, including vector search, index lifecycle, ingestion pipelines, and query performance. 
• Define and enforce patterns for prompt management, evaluation, observability, guardrails, cost control, and model lifecycle across the AI estate. 
• Partner with engineering, product, and delivery leaders to identify, scope, and deliver AI use cases that drive measurable business outcomes. 

Engineering, Scripting, and Automation 

• Write high-quality, production-grade AI-assisted code across Python, TypeScript/Node, and Bash to automate platform operations, integrate systems, and build internal tooling. 
• Design and build APIs and integrations between AI services, internal business systems, and the security tooling stack. 
• Apply infrastructure-as-code (Terraform preferred) and GitOps patterns across the AI and DevOps estate. 
• Embed security, observability, and cost discipline into every platform and pipeline you build. 

Cross-functional Contribution 

• Contribute to the wider DevOps roadmap and on-call rotation as a senior member of the team. 
• Document architecture, runbooks, and standards so the rest of the business can adopt and extend the platforms you build. 
• Coach engineers and non-engineers on safe, effective use of AI tooling and automation. 
• Stay current with the open-weight model ecosystem, hyperscaler AI service roadmaps, and the broader AI platform engineering discipline. 

Requirements

• Minimum 5 years of professional experience in DevOps, platform engineering, or SRE, with a clear track record of operating production workloads at scale. 
• Extremely strong AWS experience across compute, networking, IAM, data, and AI/ML services. Equivalent depth in Azure also required. 
• Demonstrated experience operating LLMs in production via managed inference services (AWS Bedrock or equivalent) and self-hosting open-weight models on a major hyperscaler, with the judgement to choose between them based on cost, quality, latency, and operational fit. 
• Demonstrated ability to model the unit economics of AI workloads and design cost-effective architectures, including cost-per-task and cost-per-outcome views across multiple model and hosting options. 
• Heavy scripting and AI-assisted software engineering ability across Python and TypeScript/Node, including API design, integration patterns, and asynchronous workloads. 
• Hands-on experience with enterprise search and retrieval platforms, including vector search and retrieval-augmented generation patterns. 
• Working knowledge of low-code workflow orchestration and agent automation platforms. 
• Strong understanding of security fundamentals: identity, secrets, network segmentation, data classification, and threat modelling. 
• Excellent written and verbal communication; able to translate complex platform decisions into outcomes business stakeholders understand. 

Are you interested in this position?

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