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Location Address:
9521 San Mateo NE Albuquerque, NM 87113\-2237
Compensation Pay Range:
Minimum Offer $81,432\.00 Maximum Offer $124,321\.60 Now Hiring: Senior AI Business/Process AnalystSummary:
Build your Career. Make a Difference. Presbyterian is hiring a skilled Senior AI Business/Process Analyst. This position plays a critical role in driving successful AI/RPA Automation initiatives within the organization. This role combines expertise in business analysis, artificial intelligence, and process automation to identify, analyze, and optimize business processes for automation using AI and automation technologies. Type of Opportunity: Full time Job Exempt: Yes Job is based: Reverend Hugh Cooper Administrative Center Work Shift: Days (United States of America)
Responsibilities:
The Senior AI Automation Business Analyst/Lead collaborates with stakeholders, gathers requirements, designs AI\-enabled automation solutions, and supports the implementation and continuous improvement of AI\-driven automation projects.
- Assessment of Automation as part of intake: Evaluate incoming requests to identify opportunities for AI, RPA, and automation. Assess feasibility, potential value, complexity, and alignment with business objectives to prioritize and define appropriate automation approaches.
- Process Analysis and Optimization: Conduct detailed process analysis and evaluation to identify areas for process improvement and automation. Analyze complex business processes, identify bottlenecks, and propose process optimization solutions that leverage AI and automation capabilities and best practices.
- RPA, AI, and Automation Solution Design: Collaborate with the AI, RPA, and automation development team to translate business requirements into technical specifications and solution designs. Define intelligent automation workflows, exception handling mechanisms, and data integration requirements. Ensure the scalability, maintainability, and extensibility of AI, RPA, and automation solutions.
- Stakeholder Management: Build strong relationships with business stakeholders, including department leaders, process owners, and subject matter experts. Engage in effective communication, manage expectations, and provide regular project updates. Act as a trusted advisor, educating stakeholders on AI, RPA, and automation capabilities, benefits, and potential impact on their processes.
- Project Management: Lead or actively participate in AI, RPA, and automation projects, ensuring adherence to project timelines, scope, and quality standards. Define project plans, milestones, and deliverables. Monitor project progress, identify risks and issues, and implement mitigation strategies. Collaborate with cross\-functional teams to drive project success.
- Testing and Quality: Support testing and quality assurance for AI, RPA, and automation solutions, including functional, integration, and user acceptance testing. Ensure solutions meet business requirements, performance expectations, and reliability standards before deployment.
- Assurance: Develop and execute comprehensive test plans for AI, RPA, and automation solutions. Conduct rigorous testing, including unit testing, system testing, and user acceptance testing. Ensure the quality and reliability of AI models, RPA bots, and automation workflows, and their compliance with defined business rules and requirements.
- Continuous Improvement: Proactively identify opportunities to enhance existing AI, RPA, and automation solutions and drive continuous improvement. Analyze performance metrics, identify areas for optimization, and propose process enhancements or additional automation initiatives. Stay updated on industry trends and emerging AI, RPA, and automation technologies.
Qualifications:
- Bachelors degree in related technical/business area plus 4 years of IT or business experience. 6 years of additional experience can be substituted in lieu of degree
- Advanced knowledge of Systems Analysis with focus on customer requirements and concepts of the software development lifecycl
- Proven experience as a Business Analyst or RPA Analyst, with a focus on process automation and RPA implementation
- Strong knowledge of business analysis techniques, process mapping, and process optimization methodologies
- Experience with RPA tools such as UiPath, Automation Anywhere, or Blue Prism.
- Familiarity with Agile or Scrum methodologies and project management practices.
- Excellent analytical and problem\-solving skills, with the ability to think strategically and understand complex business processes.
- Strong communication and interpersonal skills, with the ability to engage and influence stakeholders at all levels of the organization.
- Attention to detail and a commitment to delivering high\-quality solutions.
- Certifications in Business Analysis (e.g., CBAP) and RPA (e.g., UiPath RPA Developer) are advantageous.
- The role of a Senior RPA Business Analyst is crucial in driving successful RPA implementations by effectively bridging the gap between business requirements and technical solutions. This position requires a blend of strong business acumen, analytical skills, process optimization expertise, and knowledge of RPA technologies and methodologies.
All benefits\-eligible Presbyterian employees receive a comprehensive benefits package that includes medical, dental, vision, short\-term and long\-term disability, group term life insurance and other optional voluntary benefits.
Wellness
Presbyterian's Employee Wellness rewards program is designed to provide you with engaging opportunities to enhance your health and activate your well\-being. Earn gift cards and more by taking an active role in our personal well\-being by participating in wellness activities like wellness challenges, webinar, preventive screening and more.
Why work at Presbyterian?
As an organization, we are committed to improving the health of our communities. From hosting growers' markets to partnering with local communities, Presbyterian is taking active steps to improve the health of New Mexicans.
About Presbyterian Healthcare Services
Presbyterian exists to improve the health of patients, members, and the communities we serve. We are locally owned, not\-for\-profit healthcare system of nine hospitals, a statewide health plan and a growing multi\-specialty medical group. Founded in New Mexico in 1908, we are the state's largest private employer with nearly 14,000 employees \- including more than 1600 providers and nearly 4,700 nurses.
Our health plan serves more than 580,000 members statewide and offers Medicare Advantage, Medicaid (Centennial Care) and Commercial health plans.
*AA/EOE/VET/DISABLED. PHS is a drug\-free and tobacco\-free employer with smoke free campuses.*
We're Determined to Support New Mexico's Well\-Being \| Presbyterian Healthcare Services
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Salary Context
This $81K-$124K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Presbyterian Healthcare 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 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($102K) sits 53% below the category median. Disclosed range: $81K to $124K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Presbyterian Healthcare Services AI Hiring
Presbyterian Healthcare Services has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Albuquerque, NM, US. Compensation range: $124K - $124K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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