Interested in this AI/ML Engineer role at Alliant Credit Union?
Apply Now →About This Role
Responsible enterprise leader for defining and executing the organization’s AI, advanced analytics, and data science strategy. Bridges business strategy, advanced analytics, and AI platform enablement to deliver measurable business outcomes, optimize decision\-making, and drive responsible AI adoption at scale. Lead cross\-functional teams to design, build, and operationalize advanced analytics models and enterprise AI platforms while ensuring alignment with regulatory requirements, governance standards, and ethical AI principles. Foster a data\-driven culture and position the organization to maximize value from AI and analytics investments.
Essential Responsibilities
- Define and execute the enterprise strategy for AI enablement, data science, and advanced analytics.
- Translate business priorities into scalable AI, analytics, and platform roadmaps with clear outcomes.
- Lead the design, implementation, and optimization of enterprise AI platforms, including MLOps/LLMOps capabilities.
- Own the “Industrial” Use Case portfolio (i.e., large enterprise\-grade AI use cases), including intake, prioritization, roadmap, execution, and value realization across enterprise AI and advanced analytics initiatives.
- Lead decision science efforts spanning predictive modeling, optimization, experimentation, and decision strategy to improve business performance, risk outcomes, and operational efficiency.
- Establish and scale reusable frameworks, standards, and tools to enable responsible AI adoption.
- Deliver actionable insights and recommendations through data storytelling and visualization to influence decision\-making.
- Ensure compliance with Alliant’s governance, regulatory, legal, privacy, and ethical AI requirements across solutions.
- Partner with technology, data, risk, and business leaders to align AI and analytics initiatives with enterprise goals.
- Drive adoption and value realization of AI and analytics solutions across the organization.
- Establish and track KPIs to measure platform performance, model effectiveness, and business impact.
- Evaluate and manage AI technologies, tools, and vendor partnerships.
- Foster a culture of innovation, continuous learning, and enterprise\-wide data and AI maturity.
- Plan, oversee and lead the work of the team to meet functional and individual operational objectives and goals. Coach, mentor, and develop staff, including overseeing new employee onboarding and providing career development planning and opportunities. Responsible for hire, fire, performance, discipline and problem\-resolution decisions.
Education \& Years of Experience
- Minimum \- 4 Year Bachelors Degree in Computer Science, Data Science, Engineering, Finance Data or related
- Preferred \- Graduate Degree in Computer Science, Data Science, Engineering, Finance Data or related
- Minimum \- 10 Years of AI, data science, analytics, technology leadership role or related
- Minimum \- 5 Years of People Management
In Lieu of Education
- 15 Years of AI, data science, analytics, technology leadership role including people management
Compensation \& Benefits:
Typical hiring range: $167,700\.00 to $287,500\.00 Annually. Actual compensation will be determined using factors such as experience, skills \& knowledge.
Benefits: Alliant provides a benefits package including health care, vision, dental, and 401k with employer match including:
- Annual performance bonus
- Work from home up to 3 days a week
- Paid parental leave
- Employee discount programs
- Time off including paid personal and sick days
- 11 paid holidays
- Education reimbursement
- *Note that eligibility and cost of benefits can vary depending on the number of regularly scheduled hours, and job status such as regular full\-time, regular part\-time, or temporary employment.*
*Adhere to and ensure compliance of all business transactions with policy and process of the Bank Secrecy Act. Ensures compliance with all applicable state and federal laws, company procedures and policies. Maintains integrity and ethics in all actions and conversations with or regarding credit union members and their accounts; complies with Privacy Act directives.*
*The responsibilities listed do not contain a comprehensive listing of activities, duties or responsibilities that are required of the employee for this position. Duties, responsibilities and activities may change at any time with or without notice.*
Equal Opportunity Employer
This employer is required to notify all applicants of their rights pursuant to federal employment laws. For further information, please review the Know Your Rights (https://www.eeoc.gov/poster) notice from the Department of Labor.
Salary Context
This $167K-$287K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 2130 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 4,133 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Alliant 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 in Demand for This Role
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 $185,000 based on 13,200 positions with disclosed compensation. Director-level AI roles across all categories have a median of $250,000. This role's midpoint ($227K) sits 23% above the category median. Disclosed range: $167K to $287K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
Alliant Credit Union AI Hiring
Alliant Credit Union has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Chicago, IL, US. Compensation range: $287K - $287K.
Location Context
AI roles in Chicago pay a median of $200,100 across 329 tracked positions.
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 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 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 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). 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 (106) are outnumbered by mid-level (1,901) and senior (1,663) 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 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 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 $200,700. Top-quartile roles start at $254,000, 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 Safety roles lead at $274,200 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 (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 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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