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About This Role
Are you looking for a high energy, strategic, and fast\-paced position as a Manager, AI and Automation? Join Relias, the company changing lives throughout the world by helping healthcare organizations improve their clinical and financial outcomes!
For 11,000\+ health care and human service organizations, Relias helps clients deliver better clinical and financial outcomes by elevating the performance of teams. We help organizations across the continuum of care get better at maintaining compliance, developing staff and promoting consistent, high\-quality care. Our platform employs assessments to reveal specific gaps in skills and addresses them with personalized and engaging learning, choosing from 7,000\+ online courses that meet accrediting board, state and federal requirements. We are passionate about our products and our clients; what we deliver and the impact we have on the world is truly something you can be proud to represent. Join us and make a difference.
WHAT CAN RELIAS OFFER YOU?
- Fantastic health and wellness benefits package, including an outstanding 401k match, a flexible PTO program, and a generous and inclusive parental leave policy. Additionally, Relias pays for the employee portion of the monthly healthcare premium!
- Flexible work environment with onsite and work from home options – you choose when you want to come into the office!
- Active Employee Resource Groups open to all employees!
- Comprehensive onboarding program – a great introduction to our company, customers and culture!
- Growth and career advancement opportunities!
+ Multiple development program options – leadership development, professional development curriculums, and Nanodegree options in both technology and data science
+ Professional development gained from conference attendance and participation in organizations like NC Tech
Onsite 321 Coffee Shop providing free coffee and pastries to employees
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The Manager, AI and Automation is responsible for building and operating the center of expertise for AI\-enabled workflow automation. This role partners with IT business leaders (Enterprise Applications, Revenue Technology, IT Infrastructure), Information Security, Legal, and other functional leaders/stakeholders to identify, design, build, deploy, and continuously improve practical automation and AI solutions across the Relias organization.
This position is a player/coach role and is expected to execute the standup of the CoE. This role will personally ensure delivery of AI and automation projects using approved enterprise tools, translate business problems into clear requirements and future\-state workflows, configure or coordinate solutions as necessary, oversee testing and adoption, monitor usage and associated cost aspects of the models, and measure realized value as established at the beginning of projects. The role will establish a disciplined intake process, prioritization model, implementation standards, reusable templates, documentation practices, governance checkpoints, and support expectations for automation work.
As the shared\-service capability matures, this position is expected to build and manage a team of individual contributors, which may include workflow automation developers, business process analysts, and potentially AI and data engineers. The role will balance hands\-on delivery with portfolio ownership, people leadership, stakeholder enablement, standards management, and scalable operating practices so AI and automation work is useful, secure, supportable, and measurable.
WHAT YOU’LL BE DOING:
- AI and Automation Shared\-Service Product Ownership
Own the enterprise AI and automation intake process, backlog, prioritization model, and roadmap. Evaluate opportunities based on business value, feasibility, urgency, risk, data sensitivity, systems impacted, stakeholder readiness, and supportability. Translate business needs into clear requirements, future\-state workflows, success metrics, and delivery plans.
- Hands\-On Automation Development and Workflow Configuration
Design, configure, test, deploy, and maintain workflow automations using approved tools such as Microsoft 365, Power Automate, Power Apps, Copilot, Copilot Studio, SharePoint, Dataverse, Power BI, Salesforce workflow capabilities, APIs, RPA/workflow platforms, and related enterprise technologies. Build solutions directly where appropriate and coordinate Enterprise Applications, Revenue Technology, IT, Interface Engineering when deeper platform, integration, or architecture work is required.
- AI Use Case Delivery and Enablement
Develop practical AI\-enabled workflows such as document summarization, drafting support, content extraction, knowledge retrieval, classification, workflow triage, and decision\-support processes using approved AI tools. Create prompt libraries, reusable templates, output review checklists, user guidance, and enablement materials. Ensure AI workflows include appropriate human review, quality validation, escalation paths, and documented controls.
- Business Process Analysis and Solution Design
Lead discovery with functional teams to map current\-state processes, identify root causes of manual effort or inconsistency, define future\-state workflows, capture requirements, and align stakeholders on scope. Develop process maps, solution designs, user stories, acceptance criteria, control requirements, testing plans, and operating procedures that support scalable delivery.
- Platform, Integration, Data, and Security Coordination
Partner Enterprise Applications, Revenue Technology, IT, Information Security, Legal to ensure automation and AI solutions align with architecture, identity/access, data governance, privacy, vendor, integration, and production\-support standards. Coordinate APIs, data sources, permissions, environments, monitoring, auditability, and release readiness for production solutions.
- Portfolio Management, Value Measurement, and Change Enablement
Manage initiatives from intake through launch and post\-launch optimization. Build project plans, stakeholder communications, status updates, training materials, release notes, knowledge\-base content, office hours, and adoption plans. Develop dashboards and reporting to measure outcomes such as hours saved, cycle\-time reduction, quality improvement, adoption, backlog reduction, exception rates, and control performance.
- Team Leadership and Capability Buildout
Serve initially as a hands\-on manager and, as demand grows, hire, lead, coach, and manage individual contributors such as workflow automation developers, business process analysts, and AI/data engineers. Establish team roles, delivery standards, review practices, sprint or project cadences, capacity planning, performance expectations, documentation standards, and vendor/partner coordination models.
- Governance, Supportability, and Continuous Improvement
Create reusable automation patterns, design standards, support models, intake templates, risk checklists, and lifecycle\-management practices. Monitor production automations, coordinate issue resolution, retire or improve low\-value workflows, and stay current on AI, automation, Microsoft, data, SaaS workflow, and enterprise\-architecture trends that can responsibly improve the organization.
YOU’VE GOT WHAT IT TAKES IF YOU HAVE:
- 6\+ years of experience in workflow automation, business process improvement, enterprise applications, systems implementation, IT product ownership, data/AI delivery, consulting, or related roles.
- 2\+ years of experience leading projects, workstreams, technical delivery, or individual contributors; prior direct people management is preferred for this working manager role.
- Hands\-on experience building or configuring automations using Microsoft Power Platform, Microsoft 365, low\-code/no\-code tools, APIs, RPA/workflow platforms, and SaaS enterprise systems.
- Experience working with cross\-functional stakeholders to gather requirements, document processes, test solutions, manage rollout, train users, and measure impact.
- Proven experience supporting AI\-enabled workflows, data\-driven automation, reporting, dashboards, KPI tracking, or operational analytics in a governed business environment.
Bachelor's degree in Information Systems, Computer Science, Data Analytics, Business, Engineering, Operations, or a related field; equivalent relevant experience may be considered.
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IT WOULD BE IDEAL IF YOU HAVE:
- Consulting experience (either part of a team or individually) setting up a new function
- Experience building or operating an automation, AI, business\-process, or digital\-transformation center of excellence, or product\-led delivery model.
- Experience with Microsoft Copilot, Copilot Studio, Power Automate, Power Apps, SharePoint, Dataverse, Power BI, Azure AI services, SQL, Python, and SaaS platforms.
- Experience managing workflow automation developers, business process analysts, data analysts, data engineers, AI engineers, or similar technical/business\-process roles.
- Familiarity with AI governance, data privacy, information security, identity/access management, audit documentation, vendor risk, procurement intake, and regulated\-data handling.
- Microsoft Power Platform, Copilot Studio, Azure, data/AI, Agile, project management, business analysis, or process improvement certification.
*Relias is an Equal Opportunity Employer and a Drug\-Free workplace*
IN OFFICE REQUIREMENT:
Relias values collaboration and wants to ensure that our team members have opportunities to work with their teams regularly for professional development opportunities. Our flexible hybrid work environment requires that you live in the state of North Carolina, within a commutable distance to our office (\~1\-hour commute). You would be expected to work in our Morrisville, NC Headquarters approximately 40 days/quarter.
Company: Relias LLC
Country: United States of America
State/Region: North Carolina
City: Morrisville
Postal Code: 27560
Job ID: 289675
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 Relias, 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. Mid-level AI roles across all categories have a median of $165,000.
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.
Relias AI Hiring
Relias has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Morrisville, NC, US.
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.
Frequently Asked Questions
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