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About This Role
The SVP serves as a key advisor to executive leadership and the Board, providing credible challenge to management, regulatory‑ready governance, and forward‑looking insights related to emerging risks, model limitations/uncertainty, disruptive technologies such as AI and capital planning and resilience. The position is responsible for leading governance, independent oversight, and effective challenge of the company’s processes for conducting stress testing, loss forecasting, capital planning, and credit risk transfer. This role is central to ensuring the institution’s resilience under adverse conditions, maintaining prudent capital adequacy, and strategically managing exposures through risk transfer mechanisms. The position engages broadly across the company with senior leaders, in addition to industry experts and regulators to ensure effective risk management of these business processes. The successful candidate will combine quantitative expertise, strategic insight, and strong leadership to enhance enterprise\-wide risk and capital management capabilities.
Here's what you can expect from the job and what you need to be successful:
Job Duties
- Establish and maintain the enterprise Model Risk Management (MRM) program and framework, including policies, standards, procedures, and governance practices.
- Provide executive oversight of the complete MRM lifecycle, including:
o Model inventory management
o Model validation (internal and/or co‑sourced)
o Model limitations, assumptions, and uncertainty
o Issue identification, documentation, tracking and remediation monitoring
o Ongoing model performance monitoring* Ensure model risk practices effectively support credit, market, liquidity, interest rate, operational (including fraud), regulatory compliance, and capital risk assessments.
- Deliver independent challenge to model development and usage across the enterprise.
- Design and oversee the AI Governance framework and AI Risk program, including:
o AI and model definition and usage policies
o AI tool and model Risk reviews and impact assessments
o AI use\-case design initiation, assessment, implementation and monitoring
o Ethical, explainability, and bias considerations* Partner with Technology, Data, Compliance, and Business leaders to ensure AI and advanced analytics solutions are appropriately governed, explainable and defensible, and aligned with risk appetite, regulatory expectations and institutional values
- Monitor evolving regulatory guidance, industry practices, and emerging risks related to Models, AI, disruptive technologies, and capital planning.
- Provide executive leadership and independent challenge over Capital Planning framework, enterprise stress testing and scenario analysis, and capital adequacy assessments in support of strategic planning
- Ensure stress testing methodologies are appropriately designed, documented, and validated.
- Lead a talented team of professionals to provide governance and independent oversight of the company’s design and execution of enterprise stress testing and capital management programs, including scenario design, selection of business assumptions, model management, and results interpretation and assessment against risk appetite
- Develop and maintain governance, policies, and standards that define how the company executes stress testing and capital planning and manages credit risk transfer in accordance with business strategy to support safety and soundness and comply with legal, regulatory, and conservator requirements
- Oversee the institution’s capital management framework, including capital adequacy assessments and coordination of regulatory capital submissions
- Assess model design, inputs, outputs and scenarios for reasonableness, appropriate usage, severity, and alignment with the risk profile.
- Advise executive leadership on capital resiliency, exposures, and trade‑offs under adverse conditions.
- Regularly present to executive management and the Board (or Board committees) on model risk exposure, capital adequacy and stress testing results, as well as emerging risks related to models, AI, and analytics
- Communicate enterprise\-wide risk management issues and emerging risks and monitor effective and timely issue resolution
- Provide timely and independent oversight and effective challenge of the company’s risk management practices and risk\-taking activities
- Serve as a senior point of contact for regulators and examiners regarding MRM, AI and Capital Planning
- Ensure timely, transparent, and effective remediation of regulatory or internal findings.
- Set vision, strategy, and priorities for the MRM, AI Risk/Governance and Capital Planning functions.
- Build deep technical and leadership capability through talent development, succession planning, and strong risk culture.
- Assess risk to earnings and capital across a range of scenarios
- Execute an integrated oversight plan in collaboration with Operational Risk and Compliance to support the Chief Risk Officer in providing senior management and the Board with an enterprise view of risks
Essential Skills:
- 10\+ years of experience in a combination of leadership roles in risk management, loss forecasting, capital planning, or related functions within a large, complex financial institution.
- Modeling, Statistical quantification , AI development and usage
- Capital Planning
- Ability to understand stress testing methodologies, capital adequacy frameworks, and credit risk management practices
- Familiarity with relevant regulatory requirements, including CCAR/DFAST and Basel standards
- Expertise in mortgage and fixed income products, model loss estimation, and loss forecasting
- Understanding of uncertainties and limitations of models, methodologies, and judgments used to measure and manage stress losses and capital adequacy
- Effective collaboration to build trust and increase efficiency across the three lines, including the business lines and Finance Divisions, Enterprise Risk Division, and Internal Audit
- Ability to communicate effectively and efficiently
- Expertise and authority to maintain independence, critically review, and provide effective challenge of the company’s stress testing and capital management practices and credit risk transfer activities
- Ability to prioritize across multiple competing tasks, manage teams effectively, and deliver timely, high\-quality, and well\-documented oversight outcomes
- Strong organization skills, analytical mindset, and ability to work in a fast\-paced environment against tight deadlines
- Remain current on the latest financial risk management developments, regulations, and industry trends
- Ability to attract and develop risk talent
- Strong decision\-making skills with the ability to work under pressure effectively to resolve critical issues
- Strong leadership skills with the ability to influence, drive decision\-making, and lead and develop teams managing risk across complex financial portfolios
- Experience with analyzing complex financial data and risk management software and financial analysis tools (e.g., Python, R, Excel)
- Excellent verbal and written communication skills with the ability to communicate complex information to a variety of audiences, including senior management and regulators, in a clear and actionable manner
- Demonstrated track record of innovation in risk analytics, data infrastructure, or model governance practices.
- Required Education: Bachelor's degree in quantitative, finance, economics, mathematics, statistics, or related field; Master’s or PhD or professional certifications (e.g., FRM, CFA) preferred
Location: Hillsboro, OR 97124 \| (HYBRID)
Target Compensation: $320k \- $360k annually \+ annual bonusBenefits options include:* Traditional medical, dental, and vision coverage
- Generous 401K matching per pay period
- Flexible Time\-Off
- 11 paid federal holidays
- Special employee pricing on lending products such as mortgage, auto, and personal loans (eligibility for special employee pricing is subject to standard account requirements and underwriting criteria)
What makes First Tech different? Click here to learn more!First Tech is not currently offering Visa transfer/ sponsorship for this position
Salary Context
This $320K-$360K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $181K across 1996 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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At First Tech Federal Credit Union, 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 $178,940 based on 11,900 positions with disclosed compensation. This role's midpoint ($340K) sits 90% above the category median. Disclosed range: $320K to $360K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
First Tech Federal Credit Union AI Hiring
First Tech Federal Credit Union has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Hillsboro, OR, US. Compensation range: $360K - $360K.
Location Context
Across all AI roles, 16% (613 positions) offer remote work, while 3,187 require on-site attendance. Top AI hiring metros: New York (2,448 roles, $210,000 median); San Francisco (1,990 roles, $253,000 median); Los Angeles (1,686 roles, $189,000 median).
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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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