Interested in this AI/ML Engineer role at Pioneer Management Consulting?
Apply Now →Skills & Technologies
About This Role
Grow with us. Lead with us.
At Pioneer, you’ll work directly with executive teams, solving complex problems and shaping strategies that reset what’s possible. Sometimes the work is headline\-worthy. Sometimes it’s foundational. But every project earns trust – and earns us the right to take on more. You’ll get the kind of access, challenge, and growth found at big firms – while helping build a company that’s scaling fast and guided by what we value.
If you’re looking for meaning, momentum, and a seat at the table, you’re in the right place.
As a Consultant, Artificial Intelligence, you will be a part of a growing team working in a fast\-paced environment to help clients solve complex issues and deliver exceptional results in novel ways. You are a self\-driven management consultant who excels at guiding organizations to accomplish their strategic objectives through technology \& execution excellence. We're looking for an AI Specialist who is passionate about building cutting edge AI solutions — especially using Microsoft Copilot Studio and other leading web\-based AI development platforms. You are front and center with our clients and their executive teams, exploring new solutions, developing market\-defining roadmaps and rolling up your sleeves to execute the vision.
If you love working at the intersection of business problems and technical innovation, and you're excited to create AI applications that truly move the needle for clients — we want to meet you.
Responsibilities
- Strategize \& Coach: Help clients and team members better understand AI capabilities, create strategies and drive adoption of the tools you build.
- Design and Build: Lead the design, development, and deployment of AI applications using Microsoft Copilot Studio, Azure OpenAI Services, and other web\-based AI development frameworks.
- Collaborate and Co\-Create: Work closely with business strategists, developers, and client stakeholders to design solutions that are intuitive, scalable, and solve real business challenges.
- Prototype Rapidly: Build proofs\-of\-concept and minimum viable products (MVPs) to quickly validate ideas and assumptions, leveraging agile development approaches.
- Integrate: Connect AI applications to enterprise data sources, CRM systems, operational platforms, and more — ensuring solutions are robust, secure, and sustainable.
- Stay Current: Keep ahead of evolving AI technologies, Copilot extensions, LLM advancements, and best practices for secure, responsible AI deployment.
Requirements
- 3\+ years of professional experience with hands on technical AI application development, with a strong track record of delivering production\-ready solutions preferred.
- Technical Expertise:
- + Hands\-on expertise with Microsoft Copilot Studio (building custom copilots, leveraging plugins/connectors).
- + Proficiency in Azure AI services (e.g., Azure OpenAI, Cognitive Services, Bot Framework).
- + Strong skills in Power Platform (Power Apps, Power Automate) and/or low\-code development environments.
- + Familiarity with REST APIs, GraphQL, and integration architectures.
- Consulting Mindset: Ability to translate business needs into technical solutions, with an emphasis on clear communication, stakeholder engagement, and problem\-solving.
- Builder's Spirit: You enjoy creating — not just maintaining — and you thrive in fast\-paced environments where curiosity, experimentation, and collaboration are key.
- Ethical AI Awareness: A working knowledge of responsible AI practices, bias mitigation, security standards, and data privacy requirements.
Preferred Experience:
- Familiarity with Copilot extensions for Dynamics 365, Teams, or SharePoint.
- Skills in JavaScript/TypeScript, Python, or other backend web languages.
- Knowledge of industry\-specific AI applications (e.g., healthcare, manufacturing, financial services).
Location:
Must be local to Minneapolis, MN or Denver, CO market for flexible Hybrid scheudle.
Benefits
The estimated salary range for this role is $75,000 \- 132,000 annually. This is based on a wide array of factors unique to each candidate, including but not limited to skillset and years and depth of experience. This may differ from location to location. Bonuses and other incentives are awarded at the Company’s discretion and are based upon individual contributions and overall company performance. Pioneer is proud to offer a comprehensive benefits package that includes meaningful time off and paid holidays, parental leave, 401(k) including employer match, tuition reimbursement, and a broad range of health and welfare benefits including medical, dental, vision, life, long and short\-term disability, etc.
\#LI\-EH1
Salary Context
This $75K-$132K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Pioneer Management Consulting, 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($103K) sits 43% below the category median. Disclosed range: $75K to $132K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Pioneer Management Consulting AI Hiring
Pioneer Management Consulting has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Denver, CO, US, Minneapolis, MN, US. Compensation range: $132K - $285K.
Location Context
AI roles in Denver pay a median of $184,000 across 159 tracked positions. That's 8% below the national 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, 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 $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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.