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About This Role
As a key member of the Enterprise IT Team, the ServiceNow Developer, AI & Intelligent Automation designs, develops, tests, implements, and maintains solutions on the ServiceNow platform for Empyrean. This role enhances Empyrean’s IT Service Management and business processes through platform development, workflow automation, integrations, and AI-enabled capabilities.
This role independently delivers assigned development, automation, integration, and AI-enabled solutions within established technical and governance standards, and helps determine whether AI, intelligent automation, traditional workflow automation, or standard ServiceNow configuration best fits a given business need. The role requires demonstrated AI and intelligent automation experience gained outside of ServiceNow, applied within the ServiceNow platform.
ESSENTIAL DUTIES AND RESPONSIBILITIES
- Collaborates with cross-functional teams to design, configure, troubleshoot, and implement ServiceNow applications and solutions.
- Designs, builds, tests, implements, documents, and supports scalable ServiceNow solutions, including service catalog offerings, workflows, self-service capabilities, and Flow Designer automations, using established platform standards.
- Integrates ServiceNow with enterprise systems and manages the associated data exchange.
- Develops and maintains business rules, assignment and approval rules, client scripts, flows, and other platform components.
- Applies working knowledge of AI and intelligent automation concepts, including generative AI, predictive models, and automation/RPA approaches, to translate business and operational requirements into practical ServiceNow solutions.
- Contributes to decisions on whether AI, automation, or standard ServiceNow configuration best addresses a given business need.
- Configures, integrates, tests, implements, and supports approved ServiceNow AI and intelligent automation capabilities, applying required data quality, security, privacy, access control, human oversight, testing, and monitoring standards.
- Identifies automation and AI-enabled improvement opportunities and provides technical input on feasibility, dependencies, risks, and implementation needs, drawing on broader AI and automation knowledge beyond ServiceNow-specific tools.
- Contributes to proofs of concept and pilots for new AI and automation capabilities.
- Provides technical input to project planning, estimation, and delivery; organizes work through initiatives, epics, stories, and tasks; and communicates progress, dependencies, and risks.
- Participates in code reviews, knowledge sharing, and continual improvement of ServiceNow development practices; escalates complex architectural decisions or risks to Senior Developers as needed.
- Participates in ServiceNow upgrades, including impact analysis, testing, and validation, and reviews release dependencies affecting assigned applications and automations.
- Organizes and manages assigned development and support responsibilities independently, escalating competing priorities or delivery risks when appropriate.
- Monitors, troubleshoots, and improves implemented ServiceNow and AI-enabled capabilities after launch.
- Participate in an on-call rotation and provide after-hours support as required, including responding to production incidents, service disruptions, and other time-sensitive operational issues.
- Performs other job-related duties as assigned.
REQUIRED SKILLS AND ABILITIES
- Analytical, problem-solving mindset within the ServiceNow ecosystem.
- Proficiency in ServiceNow development and JavaScript, with the ability to design and implement solutions across applications, modules, workflows, integrations, and data sources.
- Working knowledge of AI and intelligent automation concepts and technologies, such as generative AI, predictive and machine learning models, and automation/RPA, gained independent of any single platform and sufficient to evaluate practical benefits, limitations, use cases, and risks.
- Ability to apply AI and intelligent automation judgment within the ServiceNow ecosystem, including data quality, security, privacy, access control, human oversight, testing, and monitoring considerations.
- Ability to translate business and operational requirements into technical designs and implementation tasks.
- Ability to identify technical risks, recommend solutions, and escalate complex decisions or exceptions when appropriate.
- Strong collaboration and communication skills, with the ability to explain technical and AI-related concepts to technical and nontechnical stakeholders.
- Proactive approach to problem identification, issue resolution, and prevention; gives and receives constructive feedback effectively.
- Ability to manage multiple tasks, deadlines, and workloads independently within established technical and organizational standards.
- Ability to contribute to the scope and design of initiatives, epics, stories, and tasks, and to perform root cause analysis on support tickets and incidents.
- Demonstrates technical ownership of assigned work and contributes to design decisions and discussions within established standards.
KNOWLEDGE, EXPERIENCE AND/OR EDUCATION REQUIREMENTS
- Bachelor’s degree preferred but not required. Equivalent experience in computer science, engineering, information technology, or a related field will be considered, as will any equivalent combination of education, experience, certifications, and training.
- Three to five years of ServiceNow development experience, or an equivalent combination of ServiceNow and closely related enterprise platform-development experience.
- Proficiency in JavaScript and ServiceNow scripting, configuration, application development, workflow automation, and integrations.
- Working knowledge of ITSM, Service Catalog, Flow Designer, integrations, and ServiceNow platform support. Experience with additional ServiceNow modules is preferred.
- Demonstrated experience independently delivering ServiceNow development work and partnering with technical and business stakeholders on automation or AI-enabled use cases.
- Demonstrated, hands-on experience with AI, machine learning, generative AI, or intelligent automation/RPA technologies gained in any enterprise setting, not limited to ServiceNow. This experience is required in addition to, and independent of, ServiceNow platform experience.
- Hands-on experience delivering at least one AI or intelligent automation capability on the ServiceNow platform is also required, such as Now Assist, Virtual Agent, AI Search, AI agents, predictive intelligence, generative AI integrations, or AI-enabled workflows. Qualifying experience includes configuration, integration, testing, implementation, or contribution to a pilot, proof of concept, or production solution; experience with every capability is not required.
- ServiceNow Certified Application Developer preferred, though equivalent ServiceNow development experience may qualify. ServiceNow Certified System Administrator and additional platform, automation, or AI-related certifications are valued.
- Experience with benefit program administration or Health and Welfare processes is a plus.
Disclaimer: This job description is not intended to be an exhaustive list of all duties, responsibilities, or qualifications associated with the job. Management reserves the right to modify or reassign job duties as business needs evolve.
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Empyrean is an Equal Opportunity Employer: including disability and protected veteran status
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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Empyrean Benefit Solutions, 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 $212,000 based on 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Empyrean Benefit Solutions AI Hiring
Empyrean Benefit Solutions has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% of all AI roles offer remote work.
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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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