Senior Deployment Strategist - Cloud & AI

Virtasant•Austin, TX

1d•Remote

About The Position

Most 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. This role is about optimization deployment, not just analysis. We are hiring Sr Deployment Strategists because the constraint is no longer simply finding inefficiency, but 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.

Requirements

Deep technical problem-solving and cloud infrastructure credibility - non-negotiable
10+ 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.
A 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.
Enterprise-scale architecture experience across areas such as multi-account or multi-region environments, high availability, horizontally scalable systems, networking, security, governance and compliance.
Strong technical problem solving: able to reason from first principles, go deep on architectural tradeoffs and failure modes, and defend technical decisions with senior engineers.
Fluency 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.
Production 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.
Real AI infrastructure depth - non-negotiable
Candidates must demonstrate strong production depth in at least two of the following three areas: GPU and accelerator infrastructure (Training or fine-tuning workloads, utilization, scheduling, capacity, accelerator tradeoffs and production constraints), Inference optimization and economics (Routing, caching, batching, context management, quantization, model selection and the relationship between latency, quality, capacity and cost), Agentic / LLM architectures (Production LLM and agentic systems, cascading calls, retries, tool invocation, resilience, observability and where cost and failure modes emerge). Strong depth in only one is too narrow; shallow keyword exposure across all three is not sufficient.
Working knowledge of the enterprise AI platform landscape - such as Bedrock, Azure OpenAI, Vertex AI and relevant neocloud offerings - and the tradeoffs between them.
Demonstrated 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.
External deployment strategist / consulting credibility - non-negotiable
Demonstrated 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.
Evidence 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.
Ability to move between CTO-level conversations and deep technical discussions with customer engineers, communicating clearly and concisely at both levels.
Consulting 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.
Internal 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.
Ownership and AI-first posture - non-negotiable
Demonstrated use of AI in your own daily engineering, architecture and consulting process, not only experience delivering AI products for others.
Operator instinct - you have built, owned or run production systems and understand what execution actually costs.
High ownership, collaboration and comfort with ambiguity. You do not depend on heavy structure or hand-holding to move an outcome forward.
Team-oriented and willing to support the expansion of a growing practice while consistently working toward the outcome.

Nice To Haves

Deep operational Kubernetes specialization.
Formal FinOps practice experience or certifications.
Experience founding or running a cloud, platform, AI or FinOps practice.
Depth in a regulated vertical such as financial services, healthcare and life sciences, energy, manufacturing or telecom.
Datacenter, colocation or hybrid-estate experience alongside cloud.
Terraform and policy-as-code.
Public speaking or writing about cloud, AI infrastructure, Kubernetes, optimization or related technical topics.

Responsibilities

Partner 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.
Lead technical discovery and architecture review. Surface integration complexity, infrastructure constraints, change-control realities and the technical tradeoffs that determine whether an engagement is feasible.
Establish technical credibility quickly with customer engineering teams, challenge assumptions constructively, and say no to work that will not land.
Scope engagements around outcomes that can be deployed and verified, not analysis or recommendations that stop at a slide deck.
Sequence multi-phase cloud and AI optimization deployment roadmaps against the client's real readiness.
Work 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.
Get changes through the client's existing workflows - ticketing, change templates, approval routing, security and compliance gates - rather than working around them.
Diagnose and adjust when technical or organizational constraints emerge mid-flight, keeping sponsors and engineering teams aligned on the outcome.
Define adoption and impact metrics before deployment and hold yourself accountable to them.
Own the executive narrative from kickoff through handoff, translating business outcomes into technical implementation and technical constraints back into business decisions.
Design POC scope, success criteria and the graduation path into production, and own the case for expansion alongside the account team.
Identify additional optimization opportunities as you learn the client's environment.
Turn field learnings into reusable playbooks so the next deployment starts further ahead.
Push product gaps, integration friction and recurring client pain back into Helias with enough technical specificity to influence the roadmap.
Operate collaboratively and independently: support the expansion of the team, remove obstacles without waiting for hand-holding, and consistently work toward the outcome.