Director, AI Platforms

SoFi•Seattle, WA

About The Position

As Director, AI Platforms, you will build and lead the team responsible for multiple AI-enabling platform services, automation, and SDLC-enabling agents for SoFi and our affiliates. This shared foundation enables teams across the company to build, deploy, and operate AI capabilities through self-service workflows, standard tooling, and clear operational contracts. You will partner closely with Engineering, Security, Risk, Legal, Compliance, and Data to ensure AI development and runtime practices meet regulatory and governance requirements by default. You will also align the AI platform roadmap with our broader internal developer platform strategy, including infrastructure automation, CI/CD, observability, and reliability standards. You’re a builder first: someone who has shipped production platforms that engineering teams actually chose to adopt, who can operate with influence across organizational boundaries, and who thrives when the roadmap is theirs to define. The idea of building an in-house variant of Open Claw that is both powerful, safe, and auditable is an appealing challenge to you.

Requirements

10+ years of engineering experience with 5+ years leading teams delivering platform or infrastructure products used by other engineering teams at scale.
Proven experience building and operating internal platforms, including multi-tenant services with clear operational contracts, reliability targets, and measurable adoption outcomes.
Experience delivering production AI platform capabilities, such as inference serving, orchestration, evaluation, and model observability, with enough depth to set architecture decisions and quality bars.
Strong technical depth in cloud and distributed systems, including AWS, Kubernetes, networking, and service reliability practices.
Hands-on experience with infrastructure automation and delivery, including infrastructure as code, CI/CD systems, and policy-as-code approaches.
Fluency in observability and operational excellence, including metrics, logging, tracing, capacity planning, and incident management.
Strong cross-functional leadership skills, including the ability to partner with Security, Risk, Legal, Compliance, and Data to translate governance requirements into developer-friendly platform guardrails.
Experience shaping vendor strategy, evaluating platforms and model providers, and balancing speed, risk, cost, and flexibility through build-versus-buy decisions.
Excellent written and verbal communication, with the ability to align executives and engineering teams on strategy, tradeoffs, and execution plans.

Nice To Haves

Experience in regulated industries such as financial services or healthcare (preferred). Familiarity with compliance frameworks (SOC2, PCI) and building security and compliance into platform defaults.

Responsibilities

Build and run a multi-tenant AI and SDLC platform that provides standardized primitives, including model access, inference serving, prompt and workflow orchestration, feature and retrieval integration, and evaluation tooling.
Establish golden paths for AI development and deployment, including templates, reference architectures, and developer experience workflows that reduce time to first production and increase reuse across teams.
Drive AI-accelerated developer productivity across the engineering organization - embedding AI capabilities into an AI native SDLC to transform how all SoFi engineers plan, code, test, build, deploy, observe, and remediate.
Partner with product and engineering leaders to drive adoption, measure outcomes, and continually refine the platform based on developer feedback and business impact. Teams use your platform because it's the fastest path to production, not because they're required to.
Define and deliver the AI and SDLC platform strategy, roadmap, and operating model, including productization, intake, prioritization, lifecycle management, and deprecation policies.
Lead vendor and ecosystem strategy for AI platform components, including build-versus-buy decisions, model provider evaluation, contracting partnership, and total cost of ownership optimization.
Build governance into the platform, including access controls, data handling controls, auditability, model and prompt change management, and approved usage patterns for regulated workloads. This isn't an approval gate — it's infrastructure that makes the right thing the easy thing.
Own platform reliability and operability, including SLOs, on-call readiness, runbooks, incident response posture, and continuous improvement through post-incident learning.
Implement platform observability for AI systems, including telemetry, tracing, and monitoring for model performance, drift, latency, safety signals, and cost drivers.
Drive automation and standardization across infrastructure provisioning and delivery pipelines, including infrastructure as code, CI/CD patterns, environment management, and policy-as-code guardrails.
Hire, develop, and retain 2-3 high-performing teams of AI and DevEx platform engineers, while setting technical standards and mentoring platform engineering practices across the organization.