{"id":1668303,"url":"https://alion.io/job/virtasant-senior-deployment-strategist-cloud-ai","title":"Senior Deployment Strategist - Cloud & AI","company":{"id":2124108,"name":"Virta Sant","domain":"virtasant.com","url":"https://alion.io/company/virtasant","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Teamtailor","truth_index":{"grade":"B","score":78,"open_postings":3,"ghost_share":0,"stale_share":0.667,"repost_share":0,"time_to_fill_p50_days":90,"computed_at":"2026-10-23T05:54:15Z"}},"role":"Solutions","role_family":"Solutions","seniority":"senior","employment_type":"full_time","work_mode":"remote","remote_scope":"stated_regions","remote_scope_basis":"posting_text","remote_working_hours":null,"hiring_geo_confidence":"inferred","locations":["Austin, United States"],"countries":["US"],"hiring_countries":["US","AR","BR","CA","CO","MX","AI","AG","AW","BS","BB","BZ","BM","BO","VG","BQ","KY","CL","CR","CW","DM","DO","EC","SV","FK","GF","GL","GD","GP","GT","GY","HT","HN","JM","MQ","MS","NI","PA","PY","PE"],"hiring_countries_total":51,"salary":null,"salary_estimate":{"min_usd":124000,"max_usd":234000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":496},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"AWS","optional":false},{"name":"AWS Bedrock","optional":false},{"name":"Azure","optional":false},{"name":"Fine-tuning","optional":false},{"name":"FinOps","optional":false},{"name":"GCP","optional":false},{"name":"Kubernetes","optional":false},{"name":"LLM","optional":false},{"name":"OpenAI","optional":false},{"name":"Quantization","optional":false},{"name":"Terraform","optional":false},{"name":"Vertex AI","optional":false}],"status":"live","first_seen_at":"2026-09-17T19:34:26Z","employer_posted_date":"2026-09-17","last_verified_at":"2026-10-23T21:08:02Z","board_verified":true,"closed_at":null,"days_open":36,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":36},"description":"Senior Deployment Strategist - Cloud & AI\nLocation: Remote, Americas (US, Canada, Latin America) | Travel: 10-20% to client sites\nWhy this role exists\nMost enterprises don't have a cloud and AI cost problem. They have a deployment problem. They may already know their GPU fleet is oversubscribed, their inference bill is compounding, or parts of their estate are running inefficiently. What they often lack is someone who can walk into a risk-averse engineering organization, identify the highest-leverage change, solve the technical problem deeply enough to earn credibility, and get the change into production.\nThat is this job. Not analysis - optimization deployment.\nWe are hiring Sr Deployment Strategists because the constraint is no longer simply finding inefficiency. It is landing the fix inside a real organization, with real approval gates, tribal knowledge, and engineers who may not have asked for a vendor's opinion. You will own that journey from the first workshop through production change and measurable business impact.\nWhat you'll own\nScope\nPartner with executive sponsors - CTO, VP Infrastructure, VP Engineering, Head of AI Platform - to turn ambiguous mandates into concrete, time-boxed deployments with a defensible business case.\n\nLead technical discovery and architecture review. Surface integration complexity, infrastructure constraints, change-control realities and the technical tradeoffs that determine whether an engagement is feasible.\n\nEstablish technical credibility quickly with customer engineering teams, challenge assumptions constructively, and say no to work that will not land.\n\nScope engagements around outcomes that can be deployed and verified, not analysis or recommendations that stop at a slide deck.\n\nDeploy\nSequence multi-phase cloud and AI optimization deployment roadmaps against the client's real readiness.\n\nWork alongside customer engineers on infrastructure and AI optimization. Own architecture decisions, technical problem solving, guardrails and policy layers, and integration into the client's pipelines and change process; customer engineers may own portions of deep implementation.\n\nGet changes through the client's existing workflows - ticketing, change templates, approval routing, security and compliance gates - rather than working around them.\n\nDiagnose and adjust when technical or organizational constraints emerge mid-flight, keeping sponsors and engineering teams aligned on the outcome.\n\nOwn the outcome\nDefine adoption and impact metrics before deployment and hold yourself accountable to them.\n\nOwn the executive narrative from kickoff through handoff, translating business outcomes into technical implementation and technical constraints back into business decisions.\n\nDesign POC scope, success criteria and the graduation path into production, and own the case for expansion alongside the account team.\n\nIdentify additional optimization opportunities as you learn the client's environment.\n\nMake the organization better\nTurn field learnings into reusable playbooks so the next deployment starts further ahead.\n\nPush product gaps, integration friction and recurring client pain back into Helias with enough technical specificity to influence the roadmap.\n\nOperate collaboratively and independently: support the expansion of the team, remove obstacles without waiting for hand-holding, and consistently work toward the outcome.\n\nWhat you'll need\nDeep technical problem-solving and cloud infrastructure credibility - non-negotiable\n10+ years of overall technical infrastructure, engineering, architecture or technical delivery experience, with meaningful public-cloud experience and deep expertise in at least one of AWS, Azure or GCP.\n\nA genuine software engineering foundation and meaningful responsibility for production systems. Strong candidates may have progressed from Software Engineer to Technical Lead / Staff / Principal Engineer and into broader cloud, platform, AI infrastructure or architecture responsibilities.\n\nEnterprise-scale architecture experience across areas such as multi-account or multi-region environments, high availability, horizontally scalable systems, networking, security, governance and compliance.\n\nStrong technical problem solving: able to reason from first principles, go deep on architectural tradeoffs and failure modes, and defend technical decisions with senior engineers.\n\nFluency in infrastructure economics and the technical drivers of spend - instance and cluster sizing, scheduling, storage, data transfer and architectural rework - with judgment about which changes are worth making.\n\nProduction Kubernetes experience is valuable but deep operational Kubernetes expertise is not required. A Kubernetes knowledge gap alone is not a disqualifier. Candidates who present Kubernetes as an area of strength should be able to demonstrate that depth in technical discussion.\n\nReal AI infrastructure depth - non-negotiable\nCandidates must demonstrate strong production depth in at least two of the following three areas. Strong depth in only one is too narrow; shallow keyword exposure across all three is not sufficient.\n\nGPU and accelerator infrastructure: Training or fine-tuning workloads, utilization, scheduling, capacity, accelerator tradeoffs and production constraints.\n\nInference optimization and economics: Routing, caching, batching, context management, quantization, model selection and the relationship between latency, quality, capacity and cost.\n\nAgentic / LLM architectures: Production LLM and agentic systems, cascading calls, retries, tool invocation, resilience, observability and where cost and failure modes emerge.\n\nWorking knowledge of the enterprise AI platform landscape - such as Bedrock, Azure OpenAI, Vertex AI and relevant neocloud offerings - and the tradeoffs between them.\n\nDemonstrated use of AI in your own daily engineering, architecture and consulting work. We will ask specifically how you use AI to reason, build, diagnose, automate and deliver.\n\nExternal deployment strategist / consulting credibility - non-negotiable\nDemonstrated experience working directly with external customers in consulting, professional services, solutions architecture, forward-deployed engineering, deployment strategy, or a comparable client-facing technical delivery role.\n\nEvidence of owning technical outcomes with a customer or counterparty who had no obligation to accept your recommendation: leading discovery, establishing technical credibility, handling pushback, solving problems live, and carrying decisions toward deployment.\n\nAbility to move between CTO-level conversations and deep technical discussions with customer engineers, communicating clearly and concisely at both levels.\n\nConsulting polish alone is not sufficient. We prioritize candidates who combine customer-facing credibility with genuine engineering and architecture depth and can defend their recommendations under technical scrutiny.\n\nInternal cross-functional influence is valuable evidence of transferable skills, but it does not replace the requirement for demonstrated external customer-facing deployment or consulting experience.\n\nOwnership and AI-first posture - non-negotiable\nDemonstrated use of AI in your own daily engineering, architecture and consulting process, not only experience delivering AI products for others.\n\nOperator instinct - you have built, owned or run production systems and understand what execution actually costs.\n\nHigh ownership, collaboration and comfort with ambiguity. You do not depend on heavy structure or hand-holding to move an outcome forward.\n\nTeam-oriented and willing to support the expansion of a growing practice while consistently working toward the outcome.\n\nHelpful, not required\nDeep operational Kubernetes specialization.\n\nFormal FinOps practice experience or certifications.\n\nExperience founding or running a cloud, platform, AI or FinOps practice.\n\nDepth in a regulated vertical such as financial services, healthcare and life sciences, energy, manufacturing or telecom.\n\nDatacenter, colocation or hybrid-estate experience alongside cloud.\n\nTerraform and policy-as-code.\n\nPublic speaking or writing about cloud, AI infrastructure, Kubernetes, optimization or related technical topics.\n\nWhy Virtasant\nDeployment is the product. Our strategists and FDEs are not a services wrapper on a platform - they are the reason the platform produces results.\n\nNo selling software. You are accountable for whether the deployment landed and produced a verified outcome.\n\nScale behind you. Work with a globally distributed network of technologists and a platform built around context-aware optimization.\n\nDirect line to product. What you find in the field influences what we build.\n\nFully remote across the Americas, with 10-20% client travel.","description_format":"text","description_chars":8790,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[{"language":"Polish","level":"All levels","optional":false}]},"benefits":[],"hiring_locations":[{"name":"United States","iso":"US","kind":"country"},{"name":"Argentina","iso":"AR","kind":"region"},{"name":"Brazil","iso":"BR","kind":"region"},{"name":"Canada","iso":"CA","kind":"region"},{"name":"Colombia","iso":"CO","kind":"region"},{"name":"Mexico","iso":"MX","kind":"region"},{"name":"Anguilla","iso":"AI","kind":"region"},{"name":"Antigua and 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