Principal, AI Strategy & Engineering Transformation

Vantor

4d•$150,000 - $242,000

About The Position

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.

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.
Bachelor's degree in Computer Science, Software Engineering, Data or AI, Systems Engineering, or a related discipline, or equivalent demonstrated experience.

Nice To Haves

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.

Responsibilities

Define and maintain Vantor's 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.
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.
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.
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.
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.
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.