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About This Role
Minnesota \- Developer
Hollstadt Overview
Hollstadt Consulting is a management and technology consulting firm dedicated to placing professionals at engagements where they will excel. When you work with us, you'll work with a refreshingly real company led and staffed by seasoned experts who are also down\-to\-earth, good people. We're committed to treating you with respect and helping you achieve your career aspirations.
Since 1990, Hollstadt has been a trusted partner to more than 150 domestic and global companies and has successfully completed over 3,000 projects. Our continued growth has created challenging and rewarding opportunities for accomplished IT and Business Consultants. Hollstadt Consulting is an equal opportunity employer including disability/veteran.
*By applying for this job, you agree to receive calls, AI\-generated calls, text messages, or emails from Hollstadt Consulting and its affiliates, and contracted partners. Frequency varies for text messages. Message and data rates may apply. Carriers are not liable for delayed or undelivered messages. You can reply STOP to cancel at any time.*
Job Description
Role: AI\-Assisted Data Engineer / Data Modernization Engineer (Contract)
Duration: 6 months
Location: Remote
Rate: $90\-$100/hour W2, dependent on skills and qualifications
Summary
Contractor is responsible for executing a large\-scale pilot data modernization initiative using AI\-assisted development to transform decentralized campus reporting assets into governed, reusable data products in Microsoft Fabric.
The position blends data engineering, automation, and applied AI to accelerate migration from legacy on\-prem, Oracle database\-dependent CAP environments to a modern, scalable data platform. The position works closely and collaboratively with the Data team’s architect, and extraction and engineering experts.
Key Responsibilities
1\. CAP Inventory \& Metadata Discovery
- Develop automated scanning tools for:
+ Access, Excel, Power BI, SQL Server, and other objects referring to REPL (on\-site Oracle database) to be used on pilot schools’ CAP servers (1 college, 3 universities, system office) and for future use across 33 institutions.
- Identify data sources, Oracle dependencies, and usage patterns
- Capture and structure metadata for AI analysis of prioritizing data migration to Fabric
2\. Data Engineering \& Fabric Development
- Develop data needed by campuses into:
+ Bronze, Silver, and Gold data layers
- Design and build pipelines and transformations in Microsoft Fabric
- Optimize for scalability, performance, and reuse
3\. AI\-Assisted Development
- Use AI tools to:
+ Generate code for inventory and metadata gathering from CAP servers
+ Automate analysis and prioritization of data moves to Fabric
+ Translate usage patterns into enterprise data products
+ Identify data gaps and recommend ingestion strategies
+ Refactor CAP server assets to Fabric\-based data sources
+ Update queries, connections, and data models via AI\-generated code
+ Develop hybrid modernization approaches (Access front\-end, SQL backend)
+ Establish repeatable AI\-assisted workflows
4\. Validation \& Testing
- Perform reconciliation between REPL and Fabric outputs
- Ensure data accuracy, completeness, and performance
7\. Documentation \& Knowledge Transfer
- Document tools, scripts, and methodologies
- Contribute to repeatable migration playbooks
- Support training and onboarding materials
Required Qualifications
- 10\+ years of experience in enterprise\-level data engineering, analytics, or data architecture
- 5\+ years of advanced SQL skills
- 5\+ years of advanced Python
- 5\+ ETL/ELT and data modeling
- 3\+ years of extensive AI\-assisted development tools (e.g. Copilot)
- 3\+ years of extensive experience with PySpark required
- 3\+ years building and optimizing large\-scale data transformations in a Fabric lakehouse environment
- Experience with modern data platforms (Microsoft Fabric)
- Experience with ETL/ELT and data modeling
Expected Outcomes
- Automated CAP inventory process
- Usage\-based prioritization and analysis
- AI\-assisted development of high\-quality, reusable Fabric data products
- Successful migration of pilot campus assets
- Scalable framework for systemwide rollout
Reporting \& Collaboration
- Reports to: System Director, Architecture and Enablement
- Collaborates with:
+ Campus IT teams
+ Data team
Benefits \+ Perks
Comprehensive Benefit Plan
Hollstadt offers medical, dental, vision, life insurance, short\-term disability, long\-term disability, paid sick leave, and retirement benefits to eligible employees. With three different medical plans to choose from, you can enroll in the coverage you need from individual to family, or anywhere in between!
Remarketing Process
Hollstadt is based on retention and relationships. We get to know your strengths and career wishes throughout your assignment and then start remarket discussions 6\-8 weeks prior to your end date. By being proactive, we are able to keep your down time between assignments as short as possible, unless you choose otherwise.
Professional Development
Hollstadt offers on\-demand training through our consultant portal. Trainings give our consultants the continuing education they need to excel on their projects. Many of our courses apply towards continuing education credits and we have an entire training hub dedicated to upskilling in Artificial Intelligence (AI).
401k \+ Matching
One popular benefit is our 401(k) match on the first 4% of your contributions. Hollstadt wants to help you reach your long\-term financial goals and understands that planning for your future is critical. Consultants also have access to support from a Financial Advisor.
Bonus Opportunities
We appreciate and reward loyalty. Join Hollstadt, stay for 5 years, and we’ll give you a $5,000 Longevity Award bonus! Additionally, we know great talent knows other great talent. If you are on contract with Hollstadt and refer one of your connections who gets placed, we’ll pay you $1,000!
Ongoing Support \& Networking
We have made a significant investment in building a support program for our consultant team \- so you never have to feel like you are going it alone. We also have a Consultant Coach program which acts like a 'work buddy' to provide a safe ear for questions or concerns at your client site.
Salary Context
This $187K-$208K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 2088 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 4,021 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Hollstadt 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 $180,000 based on 12,397 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $163,400. This role's midpoint ($197K) sits 10% above the category median. Disclosed range: $187K to $208K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($290,000) and AI Safety ($274,200). By seniority level: Entry: $97,760; Mid: $163,400; Senior: $227,400; Director: $244,800; VP: $250,000.
Hollstadt Consulting AI Hiring
Hollstadt Consulting has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in MN, US. Compensation range: $208K - $208K.
Location Context
Across all AI roles, 15% (608 positions) offer remote work, while 3,392 require on-site attendance. Top AI hiring metros: New York (2,585 roles, $210,300 median); San Francisco (2,102 roles, $253,000 median); Los Angeles (1,764 roles, $190,500 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 4,021 open positions tracked in our dataset. By seniority: 118 entry-level, 1,906 mid-level, 1,555 senior, and 442 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (608 positions). The remaining 3,392 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($290,000 median, 39 roles); AI Safety ($274,200 median, 52 roles); Research Engineer ($260,000 median, 421 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 4,021 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,818), Data Scientist (312), AI Software Engineer (280). 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 (118) are outnumbered by mid-level (1,906) and senior (1,555) 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 442 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (608 positions), with 3,392 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 $200,000. Top-quartile roles start at $253,000, 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 Engineering Manager roles lead at $290,000 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 (2,069 postings), Aws (1,260 postings), Azure (946 postings), Rag (893 postings), Gcp (783 postings), Pytorch (624 postings), Prompt Engineering (619 postings), Claude (570 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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