AI Platform Engineer Senior / Principal

What does success look like in this role?

Build the GCP side of the platform: GKE, Cloud Run, Terraform (Google provider) and Config Connector, Cloud Build and Cloud Deploy, Artifact Registry, and the Cloud Operations suite.
Stand up the AI runtime on Vertex AI (pipelines, model registry, endpoints, Feature Store) as paved-road services.
Design, build, and operate reusable cloud platform capabilities using Infrastructure as Code, CI/CD, containers, Kubernetes, identity services, and cloud networking.
Develop shared AI platform services including model gateways, API gateways, inference routing, vector and retrieval services, evaluation pipelines, and policy enforcement capabilities.
Build highly reliable platform services with clear Service Level Objectives (SLOs), observability, tracing, monitoring, automated recovery, and operational excellence.
Design secure platform foundations that incorporate identity, secrets management, network security, data boundaries, auditability, governance, and controlled access to AI models and enterprise tools.
Treat the platform as a product by creating reusable APIs, templates, documentation, paved roads, and self-service capabilities that accelerate adoption across engineering teams.
Optimize platform performance, scalability, and cost through intelligent routing, caching, quotas, lifecycle management, versioning, and cloud resource optimization.
Establish operational standards for AI platform reliability, incident response, capacity planning, and continuous improvement.
Mentor engineers, lead architecture discussions, and contribute reusable capabilities that strengthen both client platforms and RAVL's AI engineering practice.
Partner with engineering, infrastructure, security, architecture, and client stakeholders to deliver scalable AI platform capabilities aligned with enterprise governance requirements.

Sounds great, but do my skills fit?

We're looking for platform engineers with deep cloud infrastructure expertise and practical experience building modern AI-enabled platforms.

Required

Strong GCP platform engineering: GKE, IAM and Workload Identity, Shared VPC, and IaC with the Google Terraform provider.

Vertex AI MLOps depth and SLOs and telemetry via Cloud Monitoring, in a dual-cloud (GCP and Azure) estate.

Strong experience designing and operating cloud platforms using Infrastructure as Code, Kubernetes, containers, and modern cloud-native architecture.

Deep experience building CI/CD pipelines, cloud networking, identity and access management, observability platforms, and site reliability engineering practices.

Experience designing highly available distributed systems with strong operational practices including SLOs, monitoring, tracing, incident response, and capacity planning.

Working knowledge of large language models, AI inference, agent architectures, and how to expose AI capabilities safely as reusable platform services.

Experience implementing secure data governance practices including privacy, lineage, lifecycle management, access controls, and scalable data processing.

Strong understanding of enterprise security, governance, cloud operations, and cost optimization within regulated environments.

Experience collaborating across engineering, platform, infrastructure, security, and architecture teams to deliver scalable shared services.

For Senior and Principal candidates, demonstrated experience leading platform architecture, establishing engineering standards, mentoring engineers, and driving adoption of enterprise platform capabilities.

Nice to Have Skills

Experience with Azure AI Foundry, Azure OpenAI, AWS Bedrock, Vertex AI, or other managed AI platforms.
Experience with service mesh technologies, API management platforms, GPU infrastructure, model serving frameworks, or vector databases.
Experience implementing policy-as-code, multi-tenant platform architectures, software supply chain security, or advanced FinOps practices.
Experience operating enterprise Kubernetes platforms at scale.
Cloud architecture, Kubernetes (CKA/CKAD), or related certifications.
Experience working within banking, insurance, wealth management, or other regulated industries.

Mindset Traits

You enjoy building platforms that enable others to move faster and safer.
You think in systems and design for long-term scalability, resilience, and operational excellence.
You balance engineering quality, security, reliability, and cost when making architectural decisions.
You communicate effectively across engineering, infrastructure, security, and executive stakeholders.
You enjoy solving complex platform challenges through thoughtful engineering and automation.
You mentor others and create reusable capabilities that raise the bar across engineering teams.
You're excited about shaping the future of enterprise AI infrastructure and platform engineering.

Why join RAVL?

Work alongside experienced consultants solving meaningful, enterprise-scale challenges.
Help build the AI platforms that power the next generation of enterprise software delivery.
Flexible, client-aligned hybrid work model—autonomy with accountability, adapting to client delivery needs.
Variable bonus & RRSP contributions tied to performance and delivery impact.
4 weeks paid time off (plus public holidays).
Paid professional development days and continuous learning opportunities.
Comprehensive health & dental coverage, including mental health support.
Commitment to lifelong learning through mentorship, certifications, and continuous improvement.