**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.Reporting to the
AI Engineering Lead, the
AI Engineer is the engineering capacity of SavvyMoney's newly chartered AI Engineering Team. You write code. You ship internal AI tools. You build the paved roads that other engineers across SavvyMoney use when they integrate AI into their workflows.
This is an internal-build role, not a customer-facing product role. You'll work closely with the AI Engineering Lead on adoption, with our Data & Analytics organization on shared infrastructure, and with InfoSec and Legal on governance plumbing. You'll establish the patterns - RAG, agents, evals, observability, cost control - that the rest of the company adopts by default.
Key ResponsibilitiesInternal Automations and Agents- Design, build, and deploy AI-powered workflow tools for business teams (customer success, finance, legal, operations, sales, people, recruiting).
- Translate business pain points into agentic workflows using modern frameworks (LangChain, LangGraph, CrewAI, or equivalent) where the pattern fits.
- Ship production-grade tools end-to-end: requirements, prototype, deploy, instrument, iterate.
Stakeholder Partnership- Sit with the business team that requested a tool, gather the requirements yourself, and write them down before you build.
- Run UAT with the requester - they confirm the tool does the job before it ships.
- Demo what you built to the team that asked for it, and to the wider engineering group when the pattern is reusable.
Reference Architectures and Paved Roads- Define and maintain the reference patterns that engineers across SavvyMoney use when integrating AI: RAG pipelines, agent loops, evals, observability, cost control, and data classification enforcement.
- Publish pre-approved patterns and sample code so engineers don't need a fresh Legal or Security review every time.
- Own developer experience for AI integration across the company.
LLM Gateway and Cost Control- Own the internal LLM gateway: model routing, logging, abuse prevention, prompt-injection mitigation, and cost attribution.
- Build cost-per-outcome reporting (FinOps for AI) and partner with the AI Engineering Lead on portfolio-level cost decisions.
Eval Harness- Build and operate the internal eval infrastructure so any internal AI use case can be tested before it ships.
- Establish offline evaluation datasets and metrics (task success, factuality and groundedness, toxicity, latency, cost-per-task) and run online A/B tests.
- Pick eval tooling (Weights & Biases, TruLens, Promptfoo, MLflow, or equivalent) and standardize prompt versioning.
Vendor Integrations- When SavvyMoney adopts a new AI tool (Copilot, Cursor, Claude, Glean, Bedrock, or emerging vendors), you own the technical integration with our identity, data, and security stack.
- Hold vendors accountable for performance, scalability, and security commitments.
Governance Plumbing- Implement DLP integration, audit logging, prompt-injection mitigation, and data-classification enforcement across the AI surface.
- Partner with InfoSec and Legal to make the safe path the easy path.
Partner Ops Tooling- Extend internal tooling to partner ops use cases where ROI clearly exceeds the cost of a custom build.
- Coordinate with the AI Engineering Lead on which partner-facing AI investments graduate from the AI Engineering Team's portfolio into longer-term ownership.
Required Skills and Qualifications- 1-2+ years of professional software engineering experience, with at least 1 year building production AI/ML or LLM-driven applications.
- Strong proficiency in Python and modern backend development (RESTful APIs, microservices, cloud-native deployment on AWS).
- Hands-on experience with LLMs, prompt engineering, and RAG pipeline design - you have shipped, not just prototyped.
- Familiarity with vector stores, embedding models, and retrieval evaluation.
- Strong instincts on cost control, latency, and reliability for LLM-backed systems.
- Comfortable working cross-functionally with non-engineering teams to scope, build, test, and operate internal tools.
Preferred Experience- Fintech, lending, or financial services background.
- Prior experience in DevOps, platform engineering, or InfoSec - the integration-and-automation muscle translates directly to LLM-powered internal tools.
- Experience working with regulated data (PII, financial data) and the controls that go with it.
- Hands-on experience with AWS Bedrock, Anthropic, OpenAI, and one or more enterprise AI gateways.
- Bachelor's degree in Computer Science, Engineering, or a related field, or compelling self-taught equivalent.
What You'll Be Measured On- Time-to-first-value on new use cases (idea to production).
- Requester sign-off in UAT before anything ships.
- Internal tools shipped, and measured adoption per tool.
- Eval coverage on production AI workflows.
- LLM cost per outcome (FinOps for AI).
- Reference-architecture adoption by other engineers across SavvyMoney.
Base SalaryThe annual base salary for this position is between $95,000.00 and $110,000.00, depending upon 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
