Principal Cloud AI Platform Engineer

We are looking for a senior, hands-on Cloud AI Platform Engineer to lead the deployment and customisation of bespoke AI solutions for telecom customers.
The role sits between a Lead Engineer and Solution Architect. You will take solutions from prototype or development stage through to secure, production-ready deployment within customer-controlled cloud environments, primarily AWS.
This involves more than deploying an application. You will need to understand each customer’s cloud architecture, security policies, networking restrictions, data requirements and operational processes, and then adapt the solution to work within those constraints.
You will work closely with AI engineers, software developers, customer cloud teams, security teams and business stakeholders. You should therefore be comfortable both getting into the technical detail and explaining technical decisions clearly to customers.

Responsibilities

Own the end-to-end deployment of bespoke AI, GenAI and agentic AI solutions into customer cloud environments.

Assess customer AWS environments, including accounts, VPCs, IAM policies, networking, private endpoints, firewalls, DNS, proxies and environment separation.

Identify deployment constraints, dependencies and security requirements early, and agree a practical implementation approach with customer teams.

Design or contribute to the target architecture across compute, storage, networking, databases, APIs, security, observability and system integrations.

Customise LLM-powered and agentic AI solutions to meet customer-specific telecom use cases, workflows and data requirements.

Configure and adapt prompts, AI agents, tools, workflows, knowledge bases, RAG pipelines, model integrations and guardrails.

Package and deploy applications using containers, Kubernetes, serverless services or virtual machines, depending on the customer environment.

Build and maintain Infrastructure as Code and automated deployment pipelines using tools such as Terraform, CloudFormation, CDK and CI/CD platforms.

Integrate solutions with customer systems, including OSS/BSS platforms, data lakes, CRM systems, ticketing tools, network platforms, identity services and internal APIs.

Ensure deployments meet requirements around access control, encryption, privacy, audit logging, data residency, vulnerability management and software approval.

Plan for production needs such as scalability, resilience, backup, disaster recovery, monitoring, alerting, supportability and cloud cost management.

Troubleshoot issues across the cloud, application, network, data and AI layers.

Produce practical documentation, including architecture diagrams, deployment guides, configuration details, runbooks and handover materials.

Support customer workshops, architecture reviews, security reviews, testing, production readiness and operational handover.

Help establish reusable deployment patterns, reference architectures and engineering standards for future customer projects.

You will be hands-on, pragmatic and comfortable owning a technical delivery from discovery through to production handover. You should be able to balance architecture, security and customer standards with the practical need to deliver a working solution.

The role would suit someone currently working as a Lead Cloud Engineer, AI Platform Engineer, Senior DevOps Engineer, Technical Consultant or junior Cloud Solution Architect. Around seven years of relevant engineering experience would be helpful, although the quality and depth of experience matter more than an exact number of years.

Skills

Must have

Own the end-to-end deployment of bespoke AI, GenAI and agentic AI solutions into customer cloud environments.

Assess customer AWS environments, including accounts, VPCs, IAM policies, networking, private endpoints, firewalls, DNS, proxies and environment separation.

Identify deployment constraints, dependencies and security requirements early, and agree a practical implementation approach with customer teams.

Design or contribute to the target architecture across compute, storage, networking, databases, APIs, security, observability and system integrations.

Customise LLM-powered and agentic AI solutions to meet customer-specific telecom use cases, workflows and data requirements.

Configure and adapt prompts, AI agents, tools, workflows, knowledge bases, RAG pipelines, model integrations and guardrails.

Package and deploy applications using containers, Kubernetes, serverless services or virtual machines, depending on the customer environment.

Build and maintain Infrastructure as Code and automated deployment pipelines using tools such as Terraform, CloudFormation, CDK and CI/CD platforms.

Integrate solutions with customer systems, including OSS/BSS platforms, data lakes, CRM systems, ticketing tools, network platforms, identity services and internal APIs.

Ensure deployments meet requirements around access control, encryption, privacy, audit logging, data residency, vulnerability management and software approval.

Plan for production needs such as scalability, resilience, backup, disaster recovery, monitoring, alerting, supportability and cloud cost management.

Troubleshoot issues across the cloud, application, network, data and AI layers.

Produce practical documentation, including architecture diagrams, deployment guides, configuration details, runbooks and handover materials.

Support customer workshops, architecture reviews, security reviews, testing, production readiness and operational handover.

Help establish reusable deployment patterns, reference architectures and engineering standards for future customer projects.

Nice to have

Previous experience delivering technology solutions for telecom operators or telecom vendors.

Familiarity with telecom OSS/BSS systems, network operations, service assurance, customer care, field operations or network data.

Experience with AWS multi-account environments, landing zones, Control Tower and Service Control Policies.

Experience deploying into regulated, restricted, private-cloud or disconnected environments.

Familiarity with MLOps, model hosting, GPU workloads and production AI monitoring.

Relevant AWS, Kubernetes, security or architecture certifications are useful, but practical delivery experience is more important.

Other

Languages

English: B2 Upper Intermediate

Seniority

Lead