AI Platform Architect – Anthropic - UK

highmetric; USA; Remote; USD 160.000 - 210.000; Vollzeit

Zusammenfassung

highmetric is seeking a senior platform engineer to design, build, and maintain the core foundations powering enterprise AI deployments. You will work across cloud, platform, DevOps, and applied AI engineering to move LLM-powered solutions from prototype to governed production.

Responsibilities include developing reference architectures, secure integrations, and CI/CD for models like Claude, while contributing to internal AI-foundry platforms and tooling.

Qualifikationen

• 5+ years of platform, cloud, DevOps, or related engineering experience.
• Hands-on design and deployment of cloud-native apps on AWS, Azure, or GCP.
• Strong CI/CD, Git workflows, automated testing, and infrastructure as code.
• Containerization with Docker and Kubernetes or similar platforms.
• Proficiency in Python, JavaScript/TypeScript, Java, Go, or Bash; REST API design.
• Experience with LLM-powered apps, RAG, embeddings, and model monitoring.

Aufgaben

• Design, build, deploy, and maintain scalable platform capabilities for enterprise AI.
• Develop reusable reference architectures, infrastructure patterns, and templates.
• Build secure integrations to enterprise data sources, APIs, and tools.
• Collaborate with AI architects to translate client needs into platform designs.
• Maintain secure integrations with Claude-family models and related services.
• Implement CI/CD, infrastructure-as-code, monitoring, and LLMOps practices.
• Contribute reusable components and playbooks to internal platforms.

Kenntnisse

Platform engineering

Cloud engineering

DevOps

Software engineering

REST APIs

LLM-powered apps

Ausbildung

Bachelor's degree in Computer Science / Engineering / Information Systems / Data Science

Tools

Docker

Kubernetes

Terraform

Pulumi

CloudFormation

Argo CD

GitHub Actions

GitLab CI

Azure DevOps

Jobbeschreibung

Role overview

This is a senior engineering role focused on building and maintaining the technical foundations that power enterprise AI deployments. The position combines cloud, platform, DevOps, and applied AI engineering to move LLM-powered solutions from prototype into governed production. Work spans reference architectures, reusable platform components, and operational capabilities for AI applications built on Claude and comparable frontier models.

Responsibilities

• Design, build, deploy, and maintain scalable platform capabilities supporting enterprise AI, ML, LLM, retrieval-augmented generation (RAG), and agentic AI applications.
• Develop reusable reference architectures, infrastructure patterns, deployment templates, and engineering standards for an internal AI foundry practice.
• Build secure integrations connecting AI applications to enterprise data sources, APIs, workflow systems, and authorized tools.
• Partner with AI architects and forward-deployed engineers to translate client needs into reliable, supportable platform designs.
• Maintain secure integrations with Claude-family models and related AI services, including authentication, prompt and context management, structured outputs, tool use, logging, error handling, and rate-limit management.
• Implement CI/CD, infrastructure-as-code, monitoring, and LLMOps practices that improve deployment speed, quality, and operational maturity.
• Contribute reusable components, reference architectures, and playbooks to internal intelligence, data, and agent-pack platforms.

Requirements

• 5+ years of experience in platform engineering, cloud engineering, DevOps, software engineering, data engineering, or systems integration.
• Hands-on experience designing and deploying cloud-native applications on AWS, Microsoft Azure, and/or Google Cloud Platform.
• Strong experience with CI/CD, Git-based workflows, automated testing, infrastructure as code, and production release processes.
• Containerization and orchestration experience with Docker, Kubernetes, or comparable serverless or cloud-native platforms.
• Proficiency in Python, JavaScript/TypeScript, Java, Go, or Bash, plus experience designing and consuming REST APIs with authentication and authorization patterns.
• Hands-on experience with LLM-powered applications, RAG systems, prompt and context engineering, embeddings, vector search, structured outputs, tool or function calling, evaluations, and model monitoring.

Nice to have

• Familiarity with vector databases and enterprise search platforms such as Pinecone, Weaviate, pgvector, OpenSearch, Elasticsearch, or Azure AI Search.
• Infrastructure tooling experience with Terraform, Pulumi, CloudFormation, Bicep, Helm, Argo CD, GitHub Actions, GitLab CI, Azure DevOps, or Jenkins.
• ServiceNow architecture, APIs, IntegrationHub, Flow Designer, Virtual Agent, Now Assist, AI Agents, CMDB, or enterprise data-integration experience.
• FinOps practices for optimizing cloud, model, inference, storage, and data-processing costs.
• Background in consulting, professional services, enterprise architecture, or client-facing technical delivery.
• Relevant certifications in cloud, Kubernetes, security, data engineering, ServiceNow, or AI/ML.
• Bachelor's degree in Computer Science, Engineering, Information Systems, Data Science, or a related technical discipline.