To be eligible for this position, you must be a U.S. Person, defined as a U.S. citizen, permanent resident, Asylee, or Refugee.

Export Control/ITAR: Certain roles may be subject to U.S. export control laws, requiring U.S. person status as defined by 8 U.S.C. 1324b(a)(3).

Please review the job details below.

Vantor advances spatial intelligence by bringing together geospatial data, AI, and mission-focused technology to help decision makers understand what is happening now and shape what comes next. We are a team of problem solvers and builders working on consequential commercial and national-security challenges.

Please review the job details below.

Eligibility and Export Control

This role may support restricted or export-controlled engineering environments. Talent Acquisition, Legal, and Export Compliance must confirm any U.S. Person, citizenship, clearance, or other eligibility requirements before posting.

Role Overview

Vantor is seeking a Principal, AI Strategy & Engineering Transformation leader to turn AI from experimentation into a repeatable, secure, and measurable engineering capability. You will define the strategy for AI-assisted software delivery while remaining close enough to the work to build prototypes, test workflows, and help teams adopt them.

You will operate across Engineering, Product, Security, Compliance, IT, Infrastructure, Procurement, HR, and senior leadership. Your work will connect emerging AI technology to practical developer workflows, enterprise operating models, and decisions about what Vantor should standardize, scale, or stop.

This is a hands-on principal individual-contributor role with enterprise reach. You will lead primarily through technical credibility, evidence, facilitation, and influence rather than formal authority. The successful candidate can move from a working prototype to a governed pilot, an executive recommendation, and an adoption plan without losing the thread between them.

What Youll Own

AI Strategy & Developer Tooling

• Define and maintain Vantors strategy, roadmap, and investment priorities for AI-assisted software engineering.

• Evaluate AI development tools through representative workflows and evidence covering capability, security, compliance, cost, supportability, portability, and operating-model fit.

• Recommend primary tools, approved patterns, exception paths, and bounded experiments for emerging capabilities such as local inference.

AI Workflow Development & Implementation (Hands-On)

• Design, prototype, and validate agentic and AI-assisted workflows for coding, testing, documentation, code review, bug finding, repository automation, and workflow orchestration.

• Build practical solutions with modern AI tooling, APIs, scripting, prompt and context engineering, and developer-tool integrations.

• Move promising ideas from concept to prototype to reusable operating pattern, partnering with engineering teams when productionalization or platform support is required.

Engineering Workflow & Platform Transformation

• Discover how teams plan, build, review, release, and operate software; identify friction, manual work, and high-value opportunities for AI and automation.

• Redesign workflows across requirements, backlog management, development, testing, release coordination, documentation, and post-launch learning.

• Lead or support foundational changes that affect engineering delivery, including source-control governance, collaboration integrations, workflow migrations, ownership models, and phased rollout planning.

Pioneer, Champion & Adoption Programs

• Run structured pilots with real engineering teams, including readiness baselines, workflow selection, hands-on dogfooding, blocker removal, evidence collection, and scaling decisions.

• Translate successful pilots into reusable playbooks, reference implementations, automation, templates, skills, and internal marketplace or knowledge-base assets.

• Build adoption through embedded Pioneer and Champion networks; coach participants, facilitate teach-backs and demonstrations, run pulse checks, and help local leaders support teams with decreasing central assistance.

Secure, Responsible & Scalable AI

• Partner with Security, Compliance, Privacy, IT, and engineering owners to establish practical governance and approved AI usage patterns.

• Address identity, authorization, secrets, network and egress controls, data handling, storage, logging, retention, and human review as part of workflow design.

• Design bounded pilots and deployment paths for regulated, restricted, or mission-sensitive environments without treating experimental infrastructure as broadly authorized.

Measurement & Executive Decision Support

• Define success measures for adoption, developer friction, productivity, quality, useful outcomes, risk, and vendor value; establish baselines and collect before-and-after evidence.

• Turn pilot and portfolio evidence into clear recommendations about what to continue, change, standardize, scale, or stop.

• Operate a cross-functional portfolio through milestones, decision gates, dependencies, owners, risks, and concise leadership readouts; support vendor evaluation and procurement decisions with measurable evidence.

What Success Looks Like (12-18 Months)

• Vantor has an accepted, evidence-backed AI development-tool strategy with clear default and exception paths.

• Multiple AI-assisted workflows are used on real engineering work and have been converted into reusable, supported patterns.

• Engineering teams and embedded Champions can apply proven practices with decreasing dependence on the central transformation function.

• Sensitive and restricted use cases have approved operating paths, explicit boundaries, and named support owners.

• Leadership can see measurable changes in adoption, delivery friction, quality, cycle time, risk, and business value.

• Platform and workflow changes have clear ownership, practical migration plans, and evidence that they improve engineering delivery.

Why This Role Matters

AI will only create durable advantage when it changes how work is done safely, measurably, and at scale. This role connects technology strategy to the daily reality of engineering teams so Vantor can learn quickly without creating fragmented tools, unmanaged risk, or pilots that never become operating capability.

The person in this role will help Vantor convert emerging AI capabilities into better engineering outcomes, stronger internal expertise, and repeatable practices that teams can carry forward independently.

Minimum Requirements

• 8+ years of relevant experience across software engineering, developer platforms, technical product or program leadership, engineering enablement, or enterprise technology transformation.

• Hands-on experience building AI-assisted workflows, agents, prototypes, integrations, or developer automation with modern generative-AI tools.

• Strong understanding of the software delivery lifecycle, including Git-based workflows, CI/CD, testing, code review, repository automation, and secure development practices.

• Ability to use programming or scripting, APIs, and automation tooling to build prototypes, evaluation harnesses, and repeatable workflows.

• Demonstrated ability to turn ambiguous opportunities into structured experiments, measurable outcomes, roadmaps, and clear decisions.

• Experience leading cross-functional initiatives involving engineers, senior leaders, platform owners, security, compliance, infrastructure, and business stakeholders.

• Working knowledge of enterprise security, data governance, responsible AI, and the controls needed to use AI safely in software delivery.

• Proven ability to influence without direct authority, coach technical teams, facilitate working sessions, and drive adoption of unfamiliar tools or practices.

• Excellent written and verbal communication, including the ability to translate technical evidence into concise executive recommendations.

• Bachelors degree in Computer Science, Software Engineering, Data or AI, Systems Engineering, or a related discipline, or equivalent demonstrated experience.

Preferred Qualifications

• Experience deploying AI development tools in regulated, export-controlled, disconnected, or otherwise restricted environments.

• Experience evaluating or scaling AI coding assistants, agent frameworks, retrieval-augmented systems, local inference, or related developer tooling.

• Experience building Champion networks, communities of practice, embedded enablement models, or enterprise change programs.

• Experience with developer-productivity measurement, experimentation, engineering metrics, or platform-value assessment.

• Experience with GitHub Enterprise governance, source-control or collaboration-platform migrations, cloud platforms, and modern DevOps practices.

• Experience supporting vendor evaluation, procurement, implementation planning, and sustainable operating or support models.

Compensation, Benefits & Posting Requirements

Talent Acquisition Finalization Required

Before posting, Talent Acquisition must add the approved reporting line, location and work model, requisition ID, eligibility and export-control language, salary and incentive ranges, benefits, application instructions, and current EEO, accommodation, and employment-privacy notices.

Pay Transparency: To support pay transparency, Vantor includes salary ranges in all U.S. job postings. Starting pay for this role will fall within the listed range and will be based