Interested in this AI/ML Engineer role at Hollstadt Consulting?
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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: Senior Manager, Delivery \- Applied AI
Location: Remote \- local preferred but not required
Start Date: 9/28/2026
Salary: $140,600 \- $185,000, plus 20% AIP eligibility
The Senior Manager – Delivery – Applied AI serves as the single point of accountability for the execution, delivery, and operational success of enterprise AI solutions within the internal technology portfolio. This highly technical leadership role is responsible for leading multidisciplinary AI engineering teams that design, build, deploy, integrate, and support production AI, machine learning, generative AI, and agentic AI solutions that create measurable business value.
The ideal candidate combines deep AI engineering expertise with exceptional execution and delivery leadership. This individual will lead AI engineering organization, establish engineering best practices, drive end\-to\-end AI solution delivery, and partner closely with the Senior Director of AI to scale the organization's AI capabilities.
This role is responsible for ensuring AI initiatives move from concept to production with speed, quality, and operational excellence while building and developing a high\-performing, multidisciplinary AI team.
ACCOUNTABILITIES: (The primary functions, scope, and responsibilities of the role)
Technical Leadership \& AI Delivery
- Lead the end\-to\-end execution of enterprise AI initiatives from ideation through production deployment and operational support.
- Drive the delivery of AI, machine learning, generative AI, and agentic AI solutions that create measurable business value.
- Provide strong technical leadership, engineering excellence and architectural alignment across AI engineering initiatives.
- Establish and continually improve a compliant AI Development Lifecycle (AIDLC), spanning experimentation, model development, evaluation, deployment, MLOps, monitoring, governance, and ongoing optimization.
- Ensure engineering excellence through code quality, responsible AI practices, security, scalability, observability, testing, and operational readiness.
- Partner closely with Enterprise Architecture, Software Engineering, IT Operations, Cloud Engineering, Data Engineering, Security, other technology teams and Quality and Regulatory Affairs (QRA) to successfully integrate AI capabilities into enterprise platforms and applications.
- Collaborate with Product Management, business stakeholders, and functional leaders to translate business priorities into scalable AI solutions and deliver measurable outcomes.
- Remove delivery roadblocks, proactively manage risks, and ensure predictable execution across multiple concurrent AI initiatives.
Leadership \& People Management
- Builds a culture of technical excellence, accountability, collaboration, innovation, knowledge sharing, continuous learning, quality, engagement, and operational ownership.
- Provides leadership and oversight for internal staff and contract resources, including performance calibration, development planning, and corrective action in partnership with HR and IT leadership.
- Communicates status, risks, dependencies, and forward\-looking needs to IT leadership, the Senior Director of AI, business partners, and other executive stakeholders.
- Lead, mentor, and develop a multidisciplinary AI organization consisting of AI engineers, machine learning engineers, MLOps engineers, AI platform engineers, and other AI technical specialists.
- Manage team capacity, sprint execution, prioritization, staffing, and resource allocation across AI initiatives.
- Recruit, hire, onboard, and retain top AI talent as the organization grows.
- Establish clear career development paths, coaching plans, performance expectations, and technical growth opportunities for team members.
- Foster collaboration across engineering disciplines and promote knowledge sharing and engineering best practices.
- Establishes and executes talent strategies, including hiring, onboarding, coaching, career development, performance expectations, succession planning, and retention of top AI talent.
Operational Excellence
- Own engineering execution metrics including delivery predictability, quality, velocity, operational stability, and production reliability.
- Drive continuous improvement across engineering practices, including but not limited to Agentic Coding, Evals, MLOps, AI platform capabilities, and emerging modern engineering practices.
- Standardize engineering processes, reusable frameworks, documentation, and delivery methodologies.
- Ensure compliance with enterprise security, privacy, responsible AI, and governance standards.
Strategic Partnership
- Serve as the primary execution partner to the Senior Director of AI.
- Translate strategic priorities into executable engineering roadmaps, delivery plans, and staffing strategies.
- Coordinate execution across AI, technology, product, and business organizations to ensure successful delivery of enterprise AI initiatives.
- Help mature and scale the AI organization through improved engineering processes, organizational design, delivery governance, and operational excellence.
Required Qualifications
- Bachelor's degree in Computer Science, Software Engineering, Artificial Intelligence, Machine Learning, or a related technical field (Master's preferred).
- 10\+ years of software engineering experience with progressively increasing technical leadership responsibilities.
- 5\+ years leading AI/ML engineering organizations delivering production AI solutions.
- Proven experience building, deploying, and operating machine learning, generative AI, and AI\-powered applications in production.
- Deep understanding of the complete AI Development Lifecycle (AIDLC), including experimentation, model development, evaluation, deployment, MLOps, monitoring, governance, and lifecycle management.
- Strong software engineering background with expertise in cloud\-native architectures, APIs, distributed systems, CI/CD, DevOps, and modern engineering practices.
- Demonstrated experience leading cross\-functional technology initiatives involving software engineering, infrastructure, cloud operations, data engineering, security, architecture, product, and business stakeholders.
- Proven ability to build, mentor, and scale high\-performing AI engineering organizations while consistently delivering complex enterprise initiatives.
### 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 $140K-$185K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 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 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($162K) sits 24% below the category median. Disclosed range: $140K to $185K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Hollstadt Consulting AI Hiring
Hollstadt Consulting has 5 open AI roles right now. They're hiring across AI/ML Engineer, MLOps Engineer. Based in MN, US. Compensation range: $140K - $185K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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