Senior Deployment Strategist – Cloud & AI

Virtasant

. Partner with executive sponsors to turn ambiguous mandates into concrete, time-boxed deployments with defensible business cases .

Posted 9/17/2026full-timeRemote • Canada, United StatesSeniorWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates extensive experience in technical infrastructure and architecture, with a strong focus on cloud optimization and AI integration. Capable of translating complex business requirements into actionable technical solutions while ensuring stakeholder alignment and measurable outcomes.

Highest-signal resume keywords

10+ Years Technical Infrastructure ExperienceDeep Expertise in AWS, Azure, or GCPEnterprise-Scale Architecture ExperienceStrong Technical Problem-Solving SkillsExperience with AI Platforms like Bedrock or Azure OpenAI

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Technical InfrastructureArchitectureCloud OptimizationAI IntegrationProduction Systems OperationGPU and Accelerator InfrastructureInference OptimizationKubernetesTerraformPolicy-as-Code

Soft Skills

High OwnershipCollaborationComfort with AmbiguityCommunication with CTO-Level Stakeholders

Tools & Technologies

Public Cloud PlatformsAI PlatformsChange Management ToolsTicketing SystemsCompliance Tools

Certifications & Qualifications

FinOps Practice Certification

Industry Keywords

Enterprise ArchitectureMulti-Account EnvironmentsHigh AvailabilityGovernanceRegulated Industry Experience

Tech Stack

Tools & technologies

AWSAzureCloudGoogle Cloud PlatformKubernetesTerraform

About the role

Key responsibilities & impact

Partner with executive sponsors to turn ambiguous mandates into concrete, time-boxed deployments with defensible business cases
Lead technical discovery and architecture reviews
Surface integration complexity, infrastructure constraints, change-control realities and technical tradeoffs
Establish technical credibility with customer engineering teams and scope engagements around deployable, verifiable outcomes
Sequence multi-phase cloud and AI optimization deployment roadmaps
Work alongside customer engineers on infrastructure and AI optimization
Own architecture decisions, technical problem solving, guardrails, policy layers and integration into client pipelines and change processes
Navigate ticketing, change templates, approval routing, security and compliance gates
Diagnose and adjust to technical or organizational constraints while keeping stakeholders aligned
Define adoption and impact metrics before deployment and own measurable outcomes
Translate business outcomes into technical implementation and technical constraints into business decisions
Design POC scope, success criteria and graduation paths into production
Own the case for expansion alongside the account team
Identify additional optimization opportunities in client environments
Turn field learnings into reusable playbooks
Provide technically specific product feedback to influence the roadmap
Operate collaboratively and independently while supporting practice growth

Requirements

What you’ll need

10+ years of overall technical infrastructure, engineering, architecture or technical delivery experience
Meaningful public-cloud experience and deep expertise in at least one of AWS, Azure or GCP
Genuine software engineering foundation and meaningful responsibility for production systems
Enterprise-scale architecture experience, including areas such as multi-account or multi-region environments, high availability, horizontally scalable systems, networking, security, governance and compliance
Strong technical problem-solving skills, including reasoning from first principles and defending architectural decisions with senior engineers
Fluency in infrastructure economics and technical spend drivers
Strong production depth in at least two of GPU and accelerator infrastructure, inference optimization and economics, or agentic/LLM architectures
Working knowledge of enterprise AI platforms such as Bedrock, Azure OpenAI, Vertex AI and relevant neocloud offerings
Demonstrated use of AI in daily engineering, architecture and consulting work
Demonstrated experience working directly with external customers in consulting, professional services, solutions architecture, forward-deployed engineering, deployment strategy or comparable client-facing technical delivery
Ability to communicate with CTO-level stakeholders and customer engineers
Experience owning technical outcomes with external customers
Production systems operation experience
High ownership, collaboration and comfort with ambiguity
Production Kubernetes experience is valuable but not required
Helpful: formal FinOps practice experience or certifications, cloud/platform/AI/FinOps practice leadership, regulated-industry experience, datacenter/colocation/hybrid-estate experience, Terraform and policy-as-code, public speaking or technical writing

Benefits

Comp & perks

Fully remote work across the Americas
10–20% travel to client sites
Work with a globally distributed network of technologists
Direct line to product; field findings influence the product roadmap