Lead, AI Engineer (Dublin, CA or USA Remote)

**To be considered for this position, candidates must be legally authorized to work in the United States on a full-time basis without the need for employer sponsorship now or in the future.** SavvyMoney is a leading San Francisco East Bay fintech company. We provide integrated credit score and personal finance solutions to 1,600 + bank and credit union partners nationally. The SavvyMoney solutions integrate with more than 43 digital banking platforms. SavvyMoney was recently recognized by the San Francisco Business Times and the Silicon Valley Journal as one of the “Top 25 Places to Work in the San Francisco Bay Area” and is an Inc. 5000 Fastest Growing Company. Reporting to the VP, Information Security & DevOps, the Lead, AI Engineer owns both halves of AI at SavvyMoney: the systems and the adoption. You architect and personally write the internal AI tooling the company runs on, and you own getting it used. The work is the same shape as effective security or DevOps platform work — paved roads, policy, telemetry, champions, enforcement, friction reduction, repetition — applied to a new substrate. You are the connective tissue between business stakeholders, engineering, and executive leadership, translating AI capability into shipped tooling and role-based behavior change across our 200+ person company and our partner operations. This is a high-visibility role inside our newly chartered AI Engineering Team. You won’t just lead — you are the most senior builder on the team, and you manage the AI Engineer on it. You spend the majority of your week shipping internal AI tools and reference architectures, and you personally drive the adoption of what you build: literacy, champions, and a culture where AI is a default tool rather than a lighthouse project. Key Responsibilities Hands-On Build and Technical Direction Own the technical direction of every internal AI system we run, and write a large share of it yourself. Define the reference architectures the whole company builds on — RAG pipelines, agent loops, evals, the LLM gateway, observability, and cost control — and prototype the first working version of each. Ship production systems end-to-end with the AI Engineer: requirements, prototype, deploy, instrument, iterate. Set the technical bar by example — code review, eval coverage, prompt-injection defense, and cost-per-outcome discipline. Stakeholder Partnership and Delivery Run intake with business and executive stakeholders — elicit requirements, pressure-test the use case, and decide what the team builds, buys, or declines. Own the acceptance gate: the stakeholder who requested the work confirms it in UAT before it ships. Present outcomes to the people who fund and use them — monthly executive review, quarterly business reviews, and demos to the teams whose work changes. Champions Program and Community Recruit, train, and run a network of named “AI champions” — at least one per business unit — who serve as distributed sensors and accelerators for adoption. Run the champions cadence — monthly sync, quarterly offsite, recognition tied to measured impact — and the internal community of practice that shares wins, patterns, and friction across teams. Training and Literacy Design and deploy a scalable AI literacy curriculum with role-specific tracks for engineering, customer success, finance, legal, sales, recruiting, and partner ops. Own build-vs-buy on training vendors and certification pathways, and grow a measurable AI-fluency baseline quarter over quarter. Communications and Storytelling Own the internal AI Slack channel, monthly newsletter, quarterly town halls, and a success-story library tied to dollarized outcomes — translating complex AI concepts into narratives that land with technical and executive audiences alike. Office Hours and Friction Removal Run weekly drop-in office hours that make the AI Engineering Team’s tools and support accessible to every team. Identify recurring friction (policy ambiguity, tool gaps, integration blockers) and partner with your AI Engineer and the VP, Information Security & DevOps to remove it. Adoption Telemetry Own the data: % active users by team, by tool, by role. Identify dark spots and design targeted interventions— such as training, champion deployment, leadership nudges, or licensing changes. Report adoption metrics into the monthly executive review and quarterly PSG scorecard. Policy Rollout and Tool Licensing When the AI Engineering Team ships an acceptable-use policy or adopts a new tool, you own getting it adopted in practice — not just published. Advise on which seats go where, based on adoption data and ROI signals rather than headcount. Partner Ops Enablement Extend the champions and training model to partner ops teams where ROI clearly exceeds the cost of a custom build. Coordinate with our partner-facing teams to surface AI use cases that scale across our 1,600+ FI relationships. Required Skills and Qualifications 5+ years of professional software engineering experience, including production LLM systems you personally architected and shipped. Deep hands-on proficiency in Python and cloud-native AWS development, with strong opinions on evals, cost-per-outcome, latency, and prompt-injection defense. Deep technical literacy with modern AI tools (Copilot, Cursor, Claude, Glean, ChatGPT) — you use them daily, not just demo them. Strong analytical mindset with experience defining adoption metrics, instrumenting telemetry, and reporting to executive audiences. Excellent written and verbal communication — you can run a requirements session with a business team and present the outcome to the executive team in the same week. Demonstrated success driving organization-wide behavior change and running a champions network or community of practice at scale (500+ employees). Comfort working cross-functionally with engineering, legal, security, HR, and business leadership. Preferred Experience Fintech, lending, or financial services background. Prior experience in InfoSec, DevOps, or a regulated-industry technical function — the policy-and-telemetry muscle translates directly. PE-portfolio company experience. Experience with AI governance frameworks (NIST AI RMF, ISO 42001, or equivalent). Bachelor’s degree in a relevant field, or compelling self-taught equivalent. What You’ll Be Measured On Internal AI systems shipped to production, and the business outcome each one moved. Eval coverage and cost-per-outcome across production AI workflows. Reference architectures adopted as the default path by engineering teams across SavvyMoney. Active adoption percentage. Champion engagement (% of named champions actively contributing each month). Training completion rate by role. Internal NPS on AI tools and on the team’s delivery. Communications engagement (Slack, newsletter, office hours). Base Salary The annual base salary for this position is between $150,000.00 and $175,000.00, depending upon geography and experience. Additionally we provide Equity Compensation Package Flexible Time Off (FTO) - take time off as needed to rest and recharge. Medical, Dental, Vision – 100% premium paid for employee Disability/Life Insurance Opportunity for learning and career growth with a top Bay Area technology company Reimbursement for remote work setup Monthly stipend for phone and internet Team building events, culture activities, all hands events Paid time off to volunteer and serve the community Half day Fridays 401k matching contribution Beautiful California East Bay offices in Dublin, CA SavvyMoney’s EEO Statement SavvyMoney relies on diversity of culture and thought to deliver on our goal of Creative People, Practical solutions serving our client needs, and ensures nondiscrimination in all programs and activities. We continuously seek talented, qualified employees in our operations regardless of race, color, sex/gender, including gender identity and expression, sexual orientation, pregnancy, national origin, religion, disability, age, marital status, citizen status, protected veteran status, or any other protected classification under country or local law. SavvyMoney is proud to be an Equal Employment Opportunity/ Affiantative Action Employer. We are committed to protecting your data. To learn more, please review the SavvyMoney Employee Privacy Policy Notice here #J-18808-Ljbffr SavvyMoney