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About This Role
\*\*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 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 Responsibilities
Internal 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 Salary
The 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
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/ Affirmative Action Employer.
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SavvyMoney Employee Privacy Policy Notice here
Salary Context
This $95K-$110K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 2162 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At SavvyMoney, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($102K) sits 52% below the category median. Disclosed range: $95K to $110K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
SavvyMoney AI Hiring
SavvyMoney has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Dublin, CA, US. Compensation range: $110K - $175K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% of all AI roles offer remote work.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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