Director, AI Platforms

SoFi SoFi · Fintech · San Francisco, CA · Engineering

Director of AI Platforms responsible for building and leading a team that provides AI-enabling platform services, automation, and SDLC agents for SoFi. The role focuses on creating a shared foundation for teams to build, deploy, and operate AI capabilities through self-service workflows, standardized tooling, and clear operational contracts, while ensuring compliance with regulatory and governance requirements. Key responsibilities include defining the platform strategy and roadmap, driving adoption, managing vendor relationships, implementing governance and observability, and leading engineering teams.

What you'd actually do

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. Define and deliver the AI and SDLC platform strategy, roadmap, and operating model, including productization, intake, prioritization, lifecycle management, and deprecation policies.

Skills

Required

  • 10+ years of engineering experience
  • 5+ years leading teams delivering platform or infrastructure products
  • Proven experience building and operating internal platforms
  • Experience delivering production AI platform capabilities
  • Strong technical depth in cloud and distributed systems (AWS, Kubernetes, networking)
  • Hands-on experience with infrastructure automation and delivery (IaC, CI/CD, policy-as-code)
  • Fluency in observability and operational excellence
  • Strong cross-functional leadership skills
  • Experience shaping vendor strategy
  • Excellent written and verbal communication

Nice to have

  • Experience in regulated industries (financial services or healthcare)
  • Familiarity with compliance frameworks (SOC2, PCI)

What the JD emphasized

  • build and lead the team
  • AI-enabling platform services
  • self-service workflows
  • standard tooling
  • clear operational contracts
  • regulatory and governance requirements
  • AI platform roadmap
  • internal developer platform strategy
  • builder first
  • shipped production platforms
  • influence across organizational boundaries
  • roadmap is theirs to define
  • powerful, safe, and auditable
  • standardized primitives
  • inference serving
  • prompt and workflow orchestration
  • feature and retrieval integration
  • evaluation tooling
  • golden paths
  • developer experience workflows
  • reduce time to first production
  • increase reuse
  • AI-accelerated developer productivity
  • AI native SDLC
  • drive adoption
  • measure outcomes
  • developer feedback
  • business impact
  • fastest path to production
  • AI and SDLC platform strategy, roadmap, and operating model
  • productization, intake, prioritization, lifecycle management, and deprecation policies
  • vendor and ecosystem strategy
  • build versus buy decisions
  • model provider evaluation
  • contracting partnership
  • total cost of ownership optimization
  • governance into the platform
  • access controls
  • data handling controls
  • auditability
  • model and prompt change management
  • approved usage patterns
  • regulated workloads
  • infrastructure that makes the right thing the easy thing
  • platform reliability and operability
  • SLOs
  • on-call readiness
  • runbooks
  • incident response posture
  • continuous improvement
  • post-incident learning
  • platform observability for AI systems
  • telemetry, tracing, and monitoring
  • model performance, drift, latency, safety signals, and cost drivers
  • automation and standardization
  • infrastructure provisioning and delivery pipelines
  • infrastructure as code
  • CI/CD patterns
  • environment management
  • policy-as-code guardrails
  • Hire, develop, and retain 2-3 high-performing teams
  • AI and DevEx platform engineers
  • setting technical standards
  • mentoring platform engineering practices
  • 10+ years of engineering experience
  • 5+ years leading teams
  • delivering platform or infrastructure products
  • used by other engineering teams at scale
  • Proven experience building and operating internal platforms
  • multi-tenant services
  • clear operational contracts
  • reliability targets
  • measurable adoption outcomes
  • Experience delivering production AI platform capabilities
  • inference serving, orchestration, evaluation, and model observability
  • enough depth to set architecture decisions and quality bars
  • Strong technical depth in cloud and distributed systems
  • AWS, Kubernetes, networking, and service reliability practices
  • Hands-on experience with infrastructure automation and delivery
  • infrastructure as code, CI/CD systems, and policy-as-code approaches
  • Fluency in observability and operational excellence
  • metrics, logging, tracing, capacity planning, and incident management
  • Strong cross-functional leadership skills
  • partner with Security, Risk, Legal, Compliance, and Data
  • translate governance requirements into developer-friendly platform guardrails
  • Experience shaping vendor strategy
  • evaluating platforms and model providers
  • balancing speed, risk, cost, and flexibility
  • build-versus-buy decisions
  • Excellent written and verbal communication
  • align executives and engineering teams on strategy, tradeoffs, and execution plans
  • Experience in regulated industries such as financial services or healthcare (preferred)
  • Familiarity with compliance frameworks (SOC2, PCI)
  • building security and compliance into platform defaults

Other signals

  • AI platform services
  • developer self-service
  • standardized tooling
  • operational contracts
  • regulatory and governance requirements
  • AI-accelerated developer productivity
  • adoption
  • platform strategy and roadmap
  • build versus buy decisions
  • governance
  • reliability and operability
  • platform observability
  • automation and standardization
  • infrastructure as code
  • CI/CD
  • policy-as-code guardrails
  • hiring and team leadership