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Keystone AI

Enterprise AI for forecasting and optimization

Senior Director - Engineering

Engineering LeadershipConfirmed live in the last 24 hours
Full-timeSeattle, WA, USA$200k - $250k /yr

About the Job

Requirements

  • At least 10 years of engineering experience, including at least 6 years leading engineering organizations in environments with a broad SaaS estate, meaningful cloud footprint, and mission-critical integrations.
  • A track record of investing in architecture for scale and resilience, with measurable improvements in availability, recovery, incident volume, or the ability to absorb growth without proportional operational cost.
  • Deep experience with integration architecture and the operational discipline to run integrations at scale, including event-driven patterns, API design, data contracts, error handling, and observability.
  • Strong grounding in modern DevOps practice, including CI/CD, infrastructure-as-code, observability, incident response, and SRE principles, plus demonstrated ability to improve these practices in an organization.
  • Practical, current experience applying generative AI to real workflows and a considered point of view on where AI creates durable leverage.
  • Hands-on comfort with hyperscaler cloud infrastructure and modern data platforms, with the ability to make credible architecture judgments without being the deepest expert in any single system.
  • A track record building and running high-performing engineering support and issue-management functions that users trust.
  • A partnership-oriented leadership style, comfort operating as a peer alongside Product, Security, Data, and business leaders, and credibility representing engineering to firm leadership.
  • Strong written and verbal communication, including explaining technical tradeoffs to non-technical stakeholders and writing clearly for decision-makers.

Responsibilities

  • Own the engineering charter for Keystone's platform portfolio end-to-end, from architectural direction through delivery, DevOps, and iteration.
  • Translate IT/Product and business priorities into clear technical roadmaps, system designs, and prioritized engineering backlogs.
  • Set standards for how systems are designed, deployed, secured, and operated across the estate so quality is consistent across teams.
  • Contribute directly to critical decisions and hands-on judgment on prototypes, technical spikes, and complex integration work when that accelerates outcomes.
  • Lead a development team in executing product and platform development efforts, and establish DevOps practices and telemetry to monitor performance and identify issues proactively.
  • Own the multi-year technical architecture for Keystone's platform portfolio—including the SaaS ecosystem, cloud infrastructure, data platform, integration fabric, and internally built products—and evolve it as the firm scales into new geographies, practices, and consultant volumes.
  • Establish service-level targets for availability, latency, recovery, and data integrity that reflect each system's criticality to consultant delivery, client engagement, and corporate operations.
  • Drive resilience investment, including redundancy, failover, backup and recovery, disaster-recovery drills, capacity planning, and retiring brittle or single-threaded components before they fail under load.
  • Partner with Security and Compliance on architecture decisions involving client data, cross-border data flows, identity, and access, making the secure path the default path.
  • Frame AI investment around specific outcomes such as hours returned to consultants, quality improvement, reduced time-to-answer, and eliminated manual tasks, favouring durable capability and responsible use over adoption for its own sake.
  • Build and operate the internal platform that makes AI secure, cost-aware, and accessible, including model access, prompt and context management, evaluation harnesses, guardrails, observability, and connections between AI and systems of record.
  • Establish patterns for embedding AI into consultant-facing and operational workflows so adoption is fast, quality is measurable, and human review is appropriately placed.
  • Participate as a member of the AI Governance Committee to direct policy and execution of the AI governance model, including data handling, model choice, client-work considerations, and internal policies for AI use.
  • Own the integration fabric connecting Keystone's SaaS platforms, internally built products, data warehouse, and AI capabilities into a coherent operating environment.
  • Establish integration patterns and standards—including event-driven, API-first, idempotent, and observable approaches—that reduce point-to-point coupling and make future integrations cheaper and safer to build.
  • Own the end-to-end DevOps lifecycle across engineering teams, including source control, CI/CD, environment management, infrastructure-as-code, release management, and post-deployment monitoring, enabling frequent, safe changes and fast rollback.
  • Set uptime targets and integration reliability SLOs, and instrument systems to make performance measurable and issues detectable early.
  • Curate middleware, iPaaS, ETL/ELT, and event-streaming tools used across the firm; make build-versus-buy decisions and retire redundant tools.
  • Own the issue-management and resolution pipeline from intake through triage, engineering resolution, and communication to affected users, with clear ownership, SLAs, and reporting to firm leadership on health and trends.
  • Build an incident-management practice with on-call rotations, severity definitions, response protocols, communication standards, postmortems, and feedback loops that reduce repeat incidents.
  • Partner with the leader of the IT Admin organization on the technical support model for consultants and operators, including tiered support, escalation paths, self-service where appropriate, and alignment between support signals and the engineering roadmap.
  • Establish and report operational metrics—including availability, mean time to detect, mean time to resolve, deployment frequency, change failure rate, integration SLOs, and backlog age—to show estate health and drive continuous improvement.
  • Partner with Product Management to translate product strategy into a technical roadmap and maintain a joint view of priorities, tradeoffs, and delivery risk.
  • Partner with consulting practice, finance, HR, and operations leaders so engineering decisions reflect business operations and leaders understand technical constraints and choices.
  • Represent engineering outcomes to firm leadership, including system health, integration risk, AI leverage delivered, incident trends, and returns on architecture investment.
  • Own vendor relationships for the platforms and infrastructure the team operates, driving accountability for roadmap commitments, uptime, integration quality, and cost.
  • Build, lead, and develop the engineering organization across platform engineering, integrations, DevOps/SRE, and support functions, including hiring, leveling, coaching, performance, and succession.
  • Shape the operating model for a coherent growing engineering team, including team topology, on-call practice, engineering rituals, technical decision-making, and the balance between central platform teams and product-aligned squads.
  • Set and uphold standards for engineering craft, code quality, code review, testing discipline, and technical documentation.
  • Manage full-time engineers, contract engineers, offshore partners, and vendor implementation teams to deliver the roadmap.
  • Own the engineering budget and forecast, including cloud, SaaS, model, tooling, and people spend, and defend it credibly to firm leadership.
  • Design solutions that work across systems and reduce fragmentation between consultant delivery, client engagement, and back-office data.
  • Identify opportunities to introduce automation and AI to improve efficiency and reduce manual effort across the estate.
  • Improve systems based on usage patterns, incident trends, feedback, and evolving needs, including new geographic, regulatory, and practice-specific requirements as the firm expands.

Desired Qualifications

  • Experience in consulting, professional services, or another people-intensive services business where consultant leverage and utilization matter.
  • Experience operating engineering teams across multiple geographies and time zones, including EU data-residency considerations.
  • Experience building internal AI platforms—including model access layers, evaluation frameworks, prompt and context management, and cost controls—and running agentic AI systems in production.
  • Experience leading an ERP, CRM, or PSA replacement, or a comparable large-platform migration, without disrupting operations.
  • Experience owning a cloud FinOps discipline, including visibility, budgets, unit economics, and cost accountability for cloud, SaaS, and model spend.
  • Experience partnering with Security and Compliance to achieve or maintain SOC 2, ISO 27001, or comparable certifications.

About the Company

Keystone AI

Enterprise AI for forecasting and optimization

Keystone.AI commercializes the DEEP Enterprise AI platform that places neural-network-based prediction and decision systems at the core of large enterprises' operating models to optimize supply chains and free cash flow. It works by transforming ERP data (orders, shipments, invoices) into fine-grained event streams using its RAIN encoding, then the DEEP platform uses these streams with its Foundation Forecasting model to predict demand and optimize production, inventory, fulfillment, and capacity planning. It differentiates itself by embedding DEEP inside clients' existing cloud environments (AWS/Azure/GCP) and ERP/planning ecosystems (SAP, Oracle, Kinaxis, o9, OMP) and by acting as a scientific predictor and decision engine built on Keystone's methodological lineage. Its goal is to become the enterprise's central science-based predictor and decision tool that reduces forecast errors, improves operational efficiency, and enhances free cash flow for global supply chains.

Company size
11-50
Company stage
Not listed
Total funding
Not listed
Headquarters
Bellevue, WA
Founded
2003

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