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About This Role
Responsibilities/Job Description:
The Technical Director of AI \& Automation is responsible for translating enterprise AI and automation strategy into technical architecture and delivery, leading the design and implementation of scalable solutions across the organization. This role demands deep technical expertise in artificial intelligence, machine learning, and intelligent automation platforms, with a strong emphasis on cloud\-native (including Microsoft Azure and Amazon Web Services (AWS) soltutions). The ideal candidate will have hands\-on experience with AI/ML model development, deployment pipelines, and MLOps practices, as well as advanced proficiency in automation platforms such as Microsoft Power Automate and Automation Anywhere. This leader will be responsible for driving innovation through scalable and modular AI architectures, ensuring robust integration and practices\- with enterprise systems and standards, and fostering a culture of technical excellence in automation, data, and AI/ML engineering. The Technical Director will work in close partnership with internal IT stakeholders (architecture, applications, infrastructure analytics, IT operations, informatics), business partners (operations, clinical or shared business services), and external partners (vendors, academic or others) to accomplish program goals for AI, Automation, and AI governance. The ideal candidate will possess excellent communication and technical skills, enabling them to effectively engage with stakeholders at all levels and across the enterprise. A deep understanding of healthcare data and regulatory requirements is essential, as this leader is responsible for ensuring compliance and maintaining the highest standards of data security and privacy. The candidate should have hands\-on experience working with classic statistics, classic AI/ML approaches, and generative AI frameworks including their optimization; deploying AI models in production environments; leading development with teams leveraging a range of programming languages (e.g., Python, R, Java), AI tools (e.g., TensorFlow, PyTorch, scikit\-learn) and associated enabling technologies including familiarity with cloud platforms (e.g., AWS, Azure, Google Cloud).
Responsibilities* AI and Automation Strategy Execution: Deliver technical execution of the organization's AI and automation strategy, ensuring alignment with overall business goals and objectives as well as judicious capability development.
- AI and Automation Solution Implementation: Oversee the deployment of AI solutions often working with solution owner(s), including machine learning models, natural language processing including generative AI, and computer vision applications
- AI Performance Monitoring: Establish and utilize metrics and processes to monitor the performance and impact of AI solutions, directing adjustments as needed to optimize outcomes and minimize organizational risk. Work with operational owners on business case development.
- Innovation: Stay current with AI, automation, analytics, and data and AI governance advancements and trends, driving continuous improvement and innovation within the organization.
- Collaboration and AI/Automation Value: Work closely with IT, data science, and business teams to identify promising AI and automation opportunities, integrate AI and automation solutions into existing and redesigned workflows. Monitor solution outcomes to demonstrate solution value.
- Global High Performing Team: Create a team culture focused on high performance through effective recruitment, coaching and development, team modeling and design, work assignments focused on improving and growing our key talent, and enabling the success and empowerment of the team. Foster a culture in which creativity and innovation thrive; identify changes in the technology stack or processes that will motivate innovative problem\-solvers. Ability to lead a global, technical staff of internal resources and strategic partners. Previous experience working closely with external vendors to internalize capabilities and build competency of internal staff in data technologies
Required Qualifications* Bachelor of Science IT, Computer Science, Data Science, Biostatistics Healthcare, Business or related field OR an equivalent combination of experience, education and/or training.
- 8 years related IT experience and a strong understanding of data science, data integration technologies and their applications
- 5 years experience managing enterprise software development teams
- 5 years leadership including direct mentoring and managing of others, including managing leaders of others
- Knowledge of clinical data integration including RESTful and SOAP oriented web service development, ETL technology (SSIS, Data Stage, Informatica, etc.), interface protocol models (HL7, X12\), EDI standards, ESB (MuleSoft) knowledge.
- Experience with database management, data warehousing, SQL, master data management, and data modeling including healthcare common data models.
- Strong leadership and project management skills, with the ability to inspire and guide cross\-functional teams.
- Expertise in AI technologies, including machine learning, deep learning, and data analytics and the maintenance of these solutions.
- Excellent communication and presentation skills, with the ability to convey complex AI, automation, data and other technology concepts to non\-technical stakeholders.
- Proven experience in leading AI and automation projects and teams, with a strong track record of successful AI and automation solution implementations.
- Experience with business intelligence, analytics, and data visualization platforms.
- Results\-oriented with strong coaching, decision making, influencing, mentoring, negotiation, problem\-solving and situational analysis skills.
- Think innovatively and strategically, keeping in mind organizational goals and objectives.
- Possess strong analytical thinking skills, ability to deconstruct complex problems and define and implement solutions
Preferred Qualifications* Master of Science Computer Science, Biostatistics, Data Science, AI, Information Technology, or a related field.
- 10 years industry experience, some in health care preferred
- 5 years experience designing and building analytics solutions across multiple cloud vendors including leveraging open\-source ecosystem
- 5 years experience working with clinical data, preferably with the integration of EHR data, data governance and with analytics supporting a range of solutions
- Large language modeling (LLM) prompt engineering and other LLM optimization techniques (e.g., RAG – retrieval augmented generation, fine\-tuning) experience
- Experience working with global strategic partners
- Experience with web services integration and development
- Thrives in a fast\-paced, collaborative environment, efficiently works under pressures, within deadlines or other time essential constraints
- Experience with JIRA and Confluence and other productivity tools
- PMP Certification
- TOGAF or other architectural certification
- ITIL Foundation Certification
- Lean IT Management Certification
- Agile \& Scrum Certification
- Relevant certifications in AI or data science
Qualifications: $174,657\.60\- $246,584\.00 Annual
Salary Context
This $174K-$246K range is above the median 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 Fairview Health Services, 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. Director-level AI roles across all categories have a median of $247,800. This role's midpoint ($210K) sits 16% above the category median. Disclosed range: $174K to $246K.
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.
Fairview Health Services AI Hiring
Fairview Health Services has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Minneapolis, MN, US. Compensation range: $246K - $246K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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.
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