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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.
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Job Description
Role: AI/ML Engineer
Location: Remote
Duration: 1 year
Rate: $78\.20/hour W2
Role Summary:
AI/ML software engineers to design and build production AI systems for healthcare. The role spans AI system design (agent architectures, evaluation, guardrails) and production software engineering (Python services, data pipelines, cloud deployment). We are hiring multiple contractors; specific strengths can differ across candidates.
Core Responsibilities:
- Design and implement Agentic AI systems — LLM integrations, prompt engineering, MCP servers, agent architectures.
- Build and maintain Python services, automation workflows, and data pipelines (including RAG with embeddings and vector databases).
- Implement evaluation frameworks and guardrails for LLM/agent systems before production.
- Deploy, monitor, and optimize ML/AI solutions in the cloud.
- Collaborate with product, data, and engineering teams; uphold code quality, performance, security, and maintainability.
Technical Requirements:
Experience: 7\+ years of software/ML engineering, with recent hands\-on AI/LLM work.
Python: Advanced; production experience with APIs, async, and testing.
AI / LLM agents: Designing and implementing autonomous or semi\-autonomous agents (tool\- using, planners, orchestrators).
Agent frameworks: Hands\-on with at least one (LangChain, LangGraph, LlamaIndex, Semantic Kernel, Google ADK).
MCP: Agent communication, coordination, or protocol\-driven AI architectures.
Evaluation \& guardrails: Prompt regression tests, hallucination and quality metrics, and guardrails for PII, jailbreaks, and unsafe outputs. \-ML lifecycle: ML pipelines, deployment, evaluation, monitoring; embedding models, vector DBs, and RAG.
Data management: Modeling, pipelines, SQL/NoSQL, data quality and governance at scale.
Cloud: Hands\-on in Azure, AWS, or GCP; cloud\-native deployment patterns and CI/CD.
HIPAA / PHI: Working knowledge of PHI handling in AI — BAA\-covered model endpoints, no PHI in training data or logs, de\-identification before prompt context.
Preferred Technical Skills:
- AI/LLM Agent and MCP tooling
- Google ADK, Copilot Studio.
- Cloud Experience – Google Cloud or Azure preferred.
- Database Knowledge – BigQuery, Firestore, Cloud SQL, etc. \-Data pipeline – Dataflow. \-Power Automate. \-Automation Tooling – UiPath, etc. \-CI/CD Pipeline – Azure DevOps Pipeline. \-Infrastructure as Code (IaC) – Terraform.
Other Requirements:
Rapid experimentation: AI moves fast; continuously evaluates new models, capabilities, and emerging patterns (MCP, A2A, agent frameworks).
Healthcare context: AI/ML in this environment requires healthcare grounding, not generic model building. \-Proactive: Proposes AI\-assisted solutions; tests what is possible and shares findings.
Independent operator: Works with minimal supervision in fast\-moving environments; strong documentation and cross\-functional collaboration. \-MLOps or LLMOps experience. \-Streaming or event\-driven architectures. \-Prior enterprise or large\-scale data management.
Required Education:
- Master's degree in Engineering, Computer Science, mathematics, health science, or a related field AND one (1\) year experience. (Escalate for approval if Master's degree is not in any of the specified fields of study).
- OR
- Bachelor's degree with three (3\) years of experience.
- OR
- HS Diploma/GED with Seven (7\) years of experience may be considered.
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.
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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% 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 $178,940 based on 11,900 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $160,000.
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 ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; 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, 16% (613 positions) offer remote work, while 3,187 require on-site attendance. Top AI hiring metros: New York (2,448 roles, $210,000 median); San Francisco (1,990 roles, $253,000 median); Los Angeles (1,686 roles, $189,000 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 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 ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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