Cloud AI Platform Engineer

Position:
Principal Cloud AI Platform Engineer

Company:
Luxoft

Location:
Romania, Romania

Employment type:
Not specified

Work Arrangement:
Remote

Short Summary:
We are looking for a senior, hands-on Cloud AI Platform Engineer to lead the deployment and customization of bespoke AI solutions for telecom customers. The role sits between a Lead Engineer and Solution Architect, focusing on secure, production-ready deployment within customer-controlled cloud environments, primarily AWS.

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

Requirement:
- 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.
- Must have experience in deploying bespoke AI solutions into customer cloud environments, particularly AWS.

Benefits:
Not specified