Interested in this AI/ML Engineer role at Broadview Federal Credit Union?
Apply Now →Skills & Technologies
About This Role
If you are ready to join a company that truly cares about its employees, our members, and our community then you have come to the right place!
Summary of Role:
--------------------
The Senior Cyber Defense Engineer / Analyst is a senior technical role within the Cyber Defense function. This position is responsible for selecting, engineering, maintaining, tuning, and optimizing cyber defense technologies that detect, monitor, and report on enterprise security risk, including emerging risks associated with artificial intelligence systems, models, data, and third\-party AI\-enabled services. The role supports the Vice President of Cyber Assurance to ensure that cyber and AI related security risks are identified, monitored, escalated, and tracked to remediation in a manner aligned with regulatory expectations and the NIST AI Risk Management Framework (AI RMF). This position also helps operationalize AI governance across the enterprise by defining roles and accountability, improving reporting for management, and identifying opportunities to responsibly expand the use of AI tools across the Information Risk and Security department.
Essential Job Functions/Responsibilities:
-------------------------------------------------
- Lead the evaluation, selection, implementation, tuning, and lifecycle management of cyber defense and security monitoring tools used to detect threats, anomalous activity, control weaknesses, and emerging technology risks, including AI\-specific security risks.
- Establish and maintain monitoring and reporting capabilities for AI security risks in alignment with the NIST AI RMF, including risks related to data integrity, model misuse, prompt injection, model evasion, unauthorized access, information leakage, third\-party AI services, and governance/control gaps.
- Develop actionable management reporting and dashboards for cyber defense and AI security risks, including trend analysis, control effectiveness, exception reporting, incident metrics, emerging risk indicators, and remediation status.
- Work closely with the Vice President of Cyber Assurance to ensure identified security risks are documented, monitored, tracked, escalated appropriately, detected, and remediated in expeditiously.
- AI security tools implemented must be continually enhanced to reduce time to detect and respond to incidents as threats and attacks continue to evolve.
- Coordinate with AI Governance, IT, and business owners to ensure AI\-enabled systems and use cases are subject to secure implementation and usage, appropriate monitoring, control validation, and risk tracking throughout their lifecycle.
- Dual focus includes looking at AI risks with AI security tools and monitoring as well as advising on the use of AI securely by the business.
- Partner with internal stakeholders and third parties to validate that AI\-related controls are operating effectively and that issues are documented with clear ownership, target dates, and status reporting.
- Support cyber investigations, detection engineering, threat analysis, and response activities involving AI\-enabled applications, automation platforms, cloud services, identity infrastructure, and enterprise security tooling.
- Assess security telemetry coverage across on\-premises, cloud, SaaS, identity, endpoint, and AI\-related environments; identify gaps and recommend enhancements to improve visibility and control assurance.
- Recommend, pilot, and help govern appropriate use of AI\-enabled tools and automation capabilities across the Information Risk and Security department to improve efficiency, detection, analysis, workflow orchestration, reporting, and control monitoring.
- Maintain documentation for monitoring standards, operating procedures, use cases, reporting logic, escalation criteria, and control mappings relevant to cyber defense and AI security oversight.
- Provide subject matter expertise to support policy development, standards, risk assessments, new use case reviews, control testing, and executive reporting related to AI adoption and AI security risk management.
Minimum Job Qualifications:
-------------------------------
- Bachelor’s degree in cybersecurity, information security, computer science, information systems, engineering, or a related field; equivalent experience may be considered.
- 10\+ years of progressive experience in cyber defense, security engineering, security operations, threat detection, or security analytics, including senior\-level responsibility for security tooling and reporting.
- Hands\-on experience with security technologies such as SIEM, SOAR, EDR/XDR, email security, vulnerability management, identity and access monitoring, cloud security tooling, network detection and response, data protection, and threat intelligence platforms.
- Experience selecting, implementing, tuning, and administering enterprise security tools and translating technical outputs into meaningful management reporting.
- Strong understanding of cybersecurity frameworks and control environments, including NIST Cybersecurity Framework, NIST SP 800\-53 concepts, and practical risk\-based control monitoring.
- Knowledge and experience of AI\-related security risks, AI governance considerations, and the NIST AI RMF, including how to apply its concepts in a regulated enterprise environment.
- Demonstrated ability to work across technical, risk, governance, and business teams to drive accountability and measurable risk reduction.
- Experience with AI or ML security, AI\-enabled applications, model governance, or security reviews of third\-party AI capabilities.
- Experience using automation and AI\-assisted tooling to improve threat detection, triage, investigation, reporting, workflow efficiency, or control monitoring.
- Relevant certifications such as CISSP, GIAC, Security\+, Azure/AWS/GCP security certifications, or training related to AI governance, AI risk, or secure AI deployment
- Strong written and verbal communication skills, including the ability to brief leadership and produce concise, decision\-useful reporting.
Compensation: $106,194\-$138,052, plus a competitive benefits package
Bilingual individuals who are fluent in a second language in addition to English are highly encouraged to apply.
All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, status as a protected veteran, or any other status protected by applicable law.
Broadview FCU is committed to ensuring individuals with disabilities and/or those who have special needs participate in the workforce and are afforded equal opportunity to apply and compete for jobs. If you would like to contact us regarding the accessibility of our Website or need assistance completing the application process, please contact us at talentacquisition@broadviewfcu.com
### About Us
At Broadview FCU, your career is so much more than a job. Grow personally and professionally with access to opportunities to apply your strengths and unleash your potential. Broadview’s success is propelled by every employee. As we work together to serve members and our communities, our collective talents produce superior financial solutions and exceptional service that are uniquely Broadview FCU.
Salary Context
This $106K-$138K 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 Broadview 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 ($122K) sits 44% below the category median. Disclosed range: $106K to $138K.
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.
Broadview Federal Credit Union AI Hiring
Broadview Federal Credit Union has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Albany, NY, US. Compensation range: $138K - $138K.
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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.