Job Summary
AI Platform Engineer / DevOps Specialist
About the Role
We are looking for an experienced Platform Engineer to join our growing AI team. This is a
hands-on technical role where you will be responsible for building and maintaining the cloud
infrastructure that powers our AI and automation initiatives.
The ideal candidate has a solid background in cloud engineering and is genuinely interested in
the AI space. You don't need to be a machine learning expert, but you should understand how
modern AI systems work and what infrastructure they need to run reliably at scale.
This role sits at the intersection of traditional DevOps and the emerging field of AI operations.
You will work closely with data scientists, software developers, and business stakeholders to
ensure our AI capabilities are production-ready, secure, and cost-effective.
Key Responsibilities
What You'll Do
Cloud Infrastructure for AI Workloads
● Design and implement cloud architecture for AI agent runtimes, ensuring high
availability and performance across AWS and Azure environments
● Configure managed AI services including Amazon Bedrock, Azure OpenAI, and
related tooling for production use
● Set up vector databases, knowledge bases, and retrieval systems that support AI
applications
● Build container orchestration environments (EKS/AKS) for custom AI workloads and
agent frameworks
● Implement data pipelines for document ingestion, embedding generation, and
knowledge synchronisation
Security and Governance
● Configure identity federation between corporate directory services and cloud
platforms
● Implement guardrails and content safety controls for AI systems.
● Set up comprehensive audit logging and compliance monitoring for AI interactions
● Manage secrets, API keys, and credentials using cloud-native secret management
services
● Ensure data residency requirements are met, particularly for EU-based deployments
CI/CD and Automation
● Build deployment pipelines for AI agents and supporting infrastructure using GitOps
practices
● Develop Infrastructure as Code templates (Terraform, CDK) for repeatable
environment provisioning
● Implement automated testing for AI deployments, including integration tests and
behavioral validation
● Create self-service tooling that allows development teams to deploy agents
independently
Monitoring and Operations
● Set up observability stacks covering logs, metrics, and distributed tracing for AI
workloads
● Build dashboards that track AI-specific metrics: latency, token usage, error rates, and
cost per interaction
● Configure alerting for anomalies in AI behaviour, including hallucination detection
and safety violations
● Participate in on-call rotation and incident response for production AI systems
Skill Requirements
What We're Looking For
Required Experience
● At least 4 years of experience in DevOps, SRE, or Platform Engineering roles
● Strong hands-on experience with AWS (required) and Azure (preferred)
● Practical experience with container orchestration, ideally Kubernetes (EKS or AKS)
● Solid understanding of networking, security, and identity management in cloud
environments
● Proficiency with Infrastructure as Code tools, particularly Terraform
● Experience building CI/CD pipelines using GitHub Actions, GitLab CI, or similar tools
AI/ML Infrastructure Experience
We're particularly interested in candidates who have worked with:
● Managed LLM services (Amazon Bedrock, Azure OpenAI, or similar)
● Vector databases and RAG (Retrieval-Augmented Generation) architectures
● AI agent frameworks such as LangChain, LangGraph, or Semantic Kernel
● Workflow orchestration for AI systems (Step Functions, Airflow, or similar)
● MLOps practices and tooling for model deployment and monitoring
Other Requirements
Nice to Have
● Experience in financial services or other regulated industries
● Familiarity with compliance frameworks (GDPR, DORA, or similar regulations)
● Background in Python or Go for tooling and automation scripts
● AWS or Azure certifications (Solutions Architect, DevOps Engineer)
● Experience with cost optimisation for cloud AI workload