Senior Deployment Strategist - Cloud & AI

Role overview

A senior individual-contributor role focused on landing cloud and AI optimization changes inside large, risk-averse engineering organizations. Rather than producing analysis or recommendations, the position owns the end-to-end journey from executive alignment through production deployment and verified business impact. The work is remote across the Americas, with roughly 10–20% travel to client sites.

Responsibilities

- Partner with executive sponsors (CTO, VP Engineering, Head of AI Platform) to convert ambiguous mandates into time-boxed deployments backed by a defensible business case. - Lead technical discovery and architecture review, surfacing integration complexity, change-control constraints, and tradeoffs that determine feasibility. - Establish credibility quickly with customer engineering teams, challenge assumptions constructively, and decline work unlikely to ship. - Sequence multi-phase cloud and AI optimization roadmaps that respect the client's real readiness and operating model. - Work alongside customer engineers on architecture, guardrails, policy layers, and pipeline integration, navigating ticketing, change templates, security gates, and compliance review. - Diagnose mid-flight technical or organizational blockers and keep executive sponsorship aligned to outcomes.

Requirements

- Demonstrated track record embedding changes inside enterprise engineering organizations, not just producing analysis. - Strong technical depth across cloud infrastructure and AI/ML systems, with the credibility to lead architecture decisions alongside client engineers. - Experience partnering with senior stakeholders and translating business goals into deployed technical outcomes. - Operator instinct from building, owning, or running production systems at scale. - High ownership, collaborative style, and comfort driving outcomes in ambiguous environments. - Team orientation and willingness to help grow a technical practice.

Nice to have

- Deep operational Kubernetes specialization. - Formal FinOps practice experience or certifications. - Background founding or running a cloud, platform, AI, or FinOps practice. - Domain depth in regulated verticals such as financial services, healthcare, energy, manufacturing, or telecom. - Datacenter, colocation, or hybrid-estate experience alongside public cloud. - Terraform and policy-as-code fluency. - Public speaking or writing on cloud, AI infrastructure, Kubernetes, or optimization.

Benefits and work setup

- Fully remote across the Americas with 10–20% client travel. - Work with a globally distributed technical network and direct influence on internal product direction.