Interested in this AI/ML Engineer role at Eide Bailly?
Apply Now →Skills & Technologies
About This Role
Overview:
Location: Fargo, ND Work Arrangement: In\-office or Hybrid *Typical Day in the Life*
A typical day as a Director of AI and Platform directs the strategy, architecture, and delivery of Eide Bailly's AI services and platform engineering capabilities, with accountability for building a unified, scalable delivery model across both configuration\-based platforms (CoPilot, Cowork, Dynamics 365, Power Platform) and code\-based AI solutions (Azure, integrations, AI services). Owns AI lifecycle management and modernizes the firm's DevOps, CI/CD, and release management practices to improve velocity, reduce risk, and deliver more predictable business outcomes. Partners closely with the rest of the Technology Delivery \& Operations Team to align delivery and security on a cloud\-first, Zero\-Trust direction, eliminating legacy processes and tooling that no longer fit the firm's architecture.* Owns and executes the firm's AI and platform engineering strategy aligned to enterprise architecture and service line architecture.
- Owns the AI agent lifecycle (design, development, governance, and production delivery) across the firm.
- Establishes and operates a unified Platform Engineering and DevOps function responsible for the firm's delivery system across Dynamics 365, Microsoft Copilot, Power Platform, Azure, and custom development.
- Partners with the Engineering and Integration teams to streamline release management, CI/CD pipelines, environment management, and deployment practices across configuration\-based and code\-based solutions.
- Partners with the Engineering and Integration teams to define and enforce engineering standards, branching strategy, code quality, automated testing, and release governance across all delivery teams.
- Leads the platform engineering and AI delivery teams.
- Implements AI governance \- intake, controls, monitoring, lifecycle management \- aligned to the Eide Bailly TRUST framework and NIST AI RMF.
- Drives citizen development through Power Platform governance, enablement, and managed maker controls.
- Partners with service lines to deliver AI\-enabled solutions on the Microsoft cloud.
- Partners with the Network and Security Teams to align delivery and security, enforce Zero Trust, RBAC, and data governance, and eliminate heavy legacy processes and tooling that do not fit the firm's cloud direction.
- Owns the platform engineering and AI roadmap, backlog prioritization, and execution tracking.
- Ensures delivery meets timeline, budget, quality, and predictability expectations across all platforms.
*Who You Are** Deep expertise in Cloud Platforms and modern CI/CD tooling.
- Strong understanding of Power Platform ALM, and Copilot extensibility (Copilot Studio, MCP, Graph connectors).
- Proven ability to design and operate a unified DevOps and release management model across both low\-code and pro\-code delivery.
- Strong understanding of AI agent architecture, Copilot/Microsoft AI ecosystem, and Agent 365 governance.
- Knowledge of enterprise security controls: Entra ID, Purview, DLP, Defender, and Zero Trust principles.
- Experience eliminating legacy delivery processes and consolidating tooling onto modern, cloud\-aligned platforms.
- Strong strategic thinking and ability to translate business goals into predictable technical delivery.
- Financial and cost optimization experience for cloud platforms and engineering tooling.
- Strong communication, leadership, and cross\-functional partnership skills, particularly with Security, Architecture, and service line leadership.
- Ability to manage multiple priorities in an agile delivery model.
*Must be authorized to work in the United States now or in the future without visa sponsorship.* *Making an Impact Together*
People join Eide Bailly for the opportunities and stay because of the culture. At Eide Bailly, we've built a collaborative workplace based on integrity, authenticity, and support for one another. You'll find opportunities for education and career growth, a team dedicated to your success, and benefits that put your family's needs first. Hear what our employees have to say about working at Eide Bailly. *Benefits*
Beyond base compensation, Eide Bailly provides benefits such as: generous paid time off, comprehensive medical, dental, and vision insurance, 401(k) profit sharing, life and disability insurance, lifestyle spending account, certification incentives, education assistance, and a referral program.
*Next Steps*
We'll be in touch! If you look like the right fit for our position, one of our recruiters will be reaching out to schedule a phone interview with you to learn more about your career interests and goals. In the meantime, we encourage you to learn more about us on Facebook, Twitter, Instagram, LinkedIn or our About Us page.
*Eide Bailly is proud to be an affirmative action/equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability, veteran status, or any other status protected under local, state or federal laws.*
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 Eide Bailly, 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. Director-level AI roles across all categories have a median of $272,150.
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.
Eide Bailly AI Hiring
Eide Bailly has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Fargo, ND, US.
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.