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About This Role
Position Summary
The AI and Automation Senior Section Manager is responsible for leading the credit union's Artificial Intelligence (AI) and Automation function, driving innovation through evaluation, piloting, development and implementation. As the key resource for AI and Automation, you will operate to support solutions that improve employee productivity, operational efficiency, business processes, and member experiences. This role serves as an internal consultant to business units, identifying opportunities to leverage emerging technologies and delivering solutions that create measurable business value.
As the Senior Section Manager, you'll lead and develop a small, focused team of AI \& Automation Engineers and Specialists while actively participating in the design, development, implementation, and support of AI\-powered solutions, intelligent agents, robotic process automation (RPA), and low\-code/no\-code applications. The Senior Section Manager researches and evaluates emerging technologies, develops proof\-of\-concepts, and executes the AI and Automation roadmap. This will be completed through the implementation and management of enterprise AI and automation platforms, the Citizen Developer Program, and related governance practices, to ensure solutions are secure, scalable, and aligned with organizational objectives.
Please note: This position is primarily located at our HSFCU Headquarters in Downtown Honolulu.
Base Annual Salary Range: $108,900 \- $142,905 per year
The posted range reflects the anticipated base pay for this position, with final compensation determined based on experience, skills, and internal equity.
Essential Duties:
Strategy and Team Management
- Works with the Enterprise Platforms Department Manager to execute the AI and Automation roadmap in support of departmental and organizational objectives.
- Leads the AI \& Automation Section, fostering a culture of innovation, continuous improvement, experimentation, and responsible use of emerging technologies.
- Oversees recruitment, retention, coaching, performance management, and professional development of AI \& Automation Engineers and Specialists.
- Collaborates with department leadership to evaluate, prioritize, and resource AI and automation initiatives based on business value, complexity, risk, and organizational readiness.
- Establishes technical standards, development practices, and solution delivery methodologies while building organizational capabilities through mentorship and continuous learning.
Innovation \& Emerging Technology
- Research, evaluates, and pilots emerging technologies to identify opportunities that improve operational efficiency, employee productivity, and member experiences.
- Assesses the business value, technical feasibility, risks, and organizational readiness of emerging technology solutions.
- Develops recommendations for technology adoption, expansion, or retirement based on business needs and strategic objectives.
- Partners with stakeholders to identify opportunities for emerging technology solutions.
AI \& Automation Solution Development
- Leads and actively participates in the design, development, implementation, and support of enterprise technology solutions, including intelligent agents, process automation, and low\-code/no\-code applications.
- Provides technical leadership on solution architecture, integrations, platform capabilities, and development best practices.
- Oversees the lifecycle of technology solutions, ensuring quality, scalability, maintainability, and supportability.
Business Consulting \& Process Transformation
- Serves as an internal consultant to business units, facilitating discovery sessions, process assessments, and stakeholder engagement activities.
- Analyzes business processes and workflows to identify opportunities for simplification, standardization, productivity improvement, and automation.
- Assists stakeholders in evaluating solution options, expected benefits, implementation considerations, and associated risks.
- Supports organizational adoption of new technologies through education, consultation, and implementation guidance.
Project Delivery and Execution
- Leads the planning, coordination, and delivery of AI, automation, and low\-code initiatives in support of departmental and organizational objectives.
- Collaborates with stakeholders, technology teams, vendors, and project resources to define scope, priorities, timelines, and success criteria.
- Manages project activities, dependencies, risks, and resource allocation to support successful solution delivery.
- Provides project status updates, risks, issues, and recommendations to department leadership and stakeholders.
- Ensures projects are delivered in accordance with established governance, change management, security, and compliance requirements.
AI Governance \& Risk Management
- Ensures solutions are developed and implemented in compliance with established governance, security, privacy, and regulatory requirements.
- Works with Information Security, Risk Management, Compliance, and Data Governance teams to identify and mitigate technology risks.
- Ensures appropriate documentation, testing, approval, and change management practices are followed.
- Recommends improvements to governance processes and controls related to emerging technology solutions.
Vendor Management
- Serves as the product owner for enterprise AI and automation platforms, ensuring capabilities align with business needs and organizational objectives.
- Manages platform roadmaps, evaluates new features and capabilities, and recommends enhancements that improve business value and user adoption.
- Maintains relationships with AI and automation vendors, participating in roadmap discussions, capability reviews, and implementation planning.
- Promotes platform adoption and expansion through effective governance, education, and use case development.
- Monitors platform utilization and adoption, recommending improvements to maximize business value and return on investment.
Minimum Qualifications/Experience
- Bachelor's degree in Information Technology, Computer Science, or related field or equivalent experience
- 5\+ years of experience in administrating, supporting, and maintaining enterprise platforms.
- 3\+ years of experience managing technical teams and managing enterprise IT projects.
- Experience partnering with business stakeholders to identify opportunities, define requirements, and deliver technology solutions that improve business outcomes.
- Experience implementing and supporting enterprise technology platforms and vendor solutions.
- Experience evaluating, developing, or supporting low\-code/no\-code, automation, or emerging technology solutions.
- Ability to identify business challenges, analyze processes, and recommend innovative AI and automation solutions that deliver measurable business value.
- Ability to translate business requirements into technical designs, solution architectures, and implementation plans.
- Ability to lead, develop, coach, and mentor technical professionals while fostering a culture of innovation, continuous learning, and accountability.
- Ability to evaluate emerging technologies and determine their practical application within a regulated financial services environment.
- Advanced oral, written, presentation, and facilitation skills, with the ability to communicate complex technical concepts to both technical and non\-technical audiences.
- Ability to build collaborative relationships and influence decision\-making across all levels of an organization.
- Advanced knowledge of Artificial Intelligence (AI), Generative AI, AI Agents, and AI\-assisted productivity solutions.
- Advanced knowledge of automation technologies, robotic process automation (RPA), workflow orchestration, and process automation methodologies.
- Advanced knowledge of software development concepts, application integrations, APIs, scripting, and modern development practices.
- Advanced knowledge of low\-code/no\-code development platforms and solution design methodologies.
- Advanced knowledge of business process analysis, process improvement, and automation opportunity identification.
- Advanced knowledge of solution architecture, technical design, and implementation of enterprise AI and automation solutions.
- Proficient knowledge of project management methodologies and tools.
- Proficient knowledge of information security, privacy, governance, and risk management principles.
- Proficient knowledge of applicable regulations, laws, and industry standards affecting financial institutions.
Preferred Qualifications/Experience
- Certifications are not required but recommended:
- Project Management Professional (PMP)
- ITIL Foundation
- Lean Six Sigma
- Microsoft Certified: Power Platform Solution Architect Expert
- Microsoft Certified: Power Platform Functional Consultant Associate
- Microsoft Certified: Azure AI Engineer Associate
- Microsoft Certified: Applied Skills for Copilot Studio
- UiPath Certified Professional
- Relevant Artificial Intelligence, Automation, Low\-Code/No\-Code, or Cloud Platform certifications
Overview:
Find your happy place. Hawaii State Federal Credit Union is more than a bank, and not just another credit union. We’re a place where “Always Right By You” isn’t just a tagline to those we serve, but a promise to our employees, who enjoy generous benefits, opportunities for career advancement and a healthy work\-life balance. As an employee, you will be part of a team that values trust, encouragement, and the holistic experience of working together. HSFCU offers a dynamic and supportive work environment where employees can enjoy competitive compensation and one of the best benefits packages in the business.
Benefits:
- Competitive Compensation: HSFCU offers competitive pay, merit increases, and incentives.
- Health Coverage: We've got you covered: full\-time employees receive 100% coverage for medical and dental premiums, plus 50% towards covering your family members. Pre\-tax deductions for your Flexible Spending Plan can be added on as early as 6 months in.
- Paid Time Off: Enjoy 13 paid holidays each year, plus Election Day and up to 2 full days off for Community Service. Your hard work is rewarded with an increase in PTO accrual with every year you're employed.
- Retirement Savings: Contribute to a 401(k) plan with up to 10% employer contributions including a 6% match and profit sharing after your first year.
- Transportation Subsidy: We make your daily commute stress\-free. Receive 100% bus pass reimbursement or up to $100 subsidy towards parking and Pre\-Tax deductions.
- Health \& Wellness: Access to wellness fairs, flu shot clinics, and on\-site fitness centers.
- Additional Benefits: Flexible spending plans, credit union discounts, life, accident, and disability insurance.
- Growth Opportunities: HSFCU invests in employee development through in\-person and online training programs, workshops, career development assistance, and tuition assistance. Employees are encouraged to further their education and unlock new opportunities.
- Work Environment: HSFCU’s modern headquarters prioritize a balance between wellness and productivity, offering a variety of amenities. The culture is inclusive, with a focus on teamwork and community, often described as an 'ohana' or family atmosphere.
- Employee Testimonials: Employees appreciate the supportive environment where everyone’s voice is heard and valued. The credit union has been recognized as one of Hawaii’s best places to work for over 15 years.
Salary Context
This $108K-$142K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Hawaii State 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($125K) sits 42% below the category median. Disclosed range: $108K to $142K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Hawaii State Federal Credit Union AI Hiring
Hawaii State Federal Credit Union has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Honolulu, HI, US. Compensation range: $142K - $142K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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