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Requisition \#:
674283
Location:
Johns Hopkins Health System, Baltimore, MD 21201
Category:
Information Technology
Schedule:
Day Shift
Employment Type:
Full Time
Req\#:674283 Epic Project Leader\-AI Productivity \& Automation
Monday\-Friday (8:30\-5\)
Hybrid/Remote In the office 1\-2 days monthly on a regular basis. Addtional days GO Lives, weekends and Special Projects.
John Hopkins
750 E. Pratt St., 5th Floor
Baltimore, MD 21202
General Position Summary: Demonstrates specialized knowledge of and expertise in relevant computer systems, applications, interfaces and business processes to deliver highly complex solutions for systems and application problems. Position divides time between lead duties and analyst duties (generally on a 50/50 basis). Lead and provide direction to assigned staff (composed of IT Specialists, Programmer Analysts, Senior Programmer Analysts, Software Engineers, Senior Software Engineers and Application Coordinators) for all tasks by reviewing work for technical viability and for adherence to institutional standards and guidelines. Lead assigned staff to implement and integrate various departmental and enterprise wide application systems (packages and/or home grown) into the current environment or new environments; and to support production application systems (including but not limited to modification, and testing of new and/or upgraded applications, operating systems, file structures, hardware, communication devices, and productivity tools as well as performance of the application). Apply analysis techniques and procedures to gather and then translate business requirements into functional/technical specifications and designs. Using functional specifications and designs, produce all or part of the deliverables. Maintain databases and application system code.
Job Scope/Complexity: Provide technical leadership based on extensive technical knowledge, skills and experience; influence clients towards innovative/integrated solutions. Analyzes and resolves highly complex system integration problems. Responsible for full life\-cycle of large sized complex projects which can span an entire department, division, or enterprise\-wide level. For enterprise\-wide level, typical projects impact the broader customer base of Johns Hopkins and its most mission critical systems. Projects often require interaction with various departments and teams both within and outside the department. Build and maintain client relationships through positive interactions. (redundant to leadership responsibilities) For project work, lead diversified staff in various environments. Interface with senior level IT management, department heads, JHM personnel, vendors and consultants while leading projects. Accountable for project outcome. Must achieve balance between orchestrating work of others and doing technical work.
Job Responsibilities: The responsibilities listed below are typical examples of the work performed by this position. Not all duties assigned to this position are included, nor is it expected that everyone in this position will be assigned every job responsibility.
AI Solution Delivery • Lead the planning and execution of AI\-enabled productivity projects, including automation and workflow optimization initiatives • Coordinate cross\-functional teams across IT, operations, and training Solution Design \& Facilitation • Facilitate stakeholder sessions to identify AI and automation opportunities • Translate business requirements into AI\-enabled solutions and workflows Pilot \& Experimentation Management • Manage pilot programs for AI productivity tools, including defining success metrics and evaluation criteria • Iterate on solutions based on feedback and measurable outcomes Integration \& Scaling • Coordinate integration of AI tools with existing enterprise systems and workflows • Develop and execute plans for scaling successful solutions across the organization
- Leadership and Project Management Responsibilities
- Analyst Responsibilties
- Implementation and Maintenance (Prepares the Application Go Live Plan)
- On\-Call Responsibility
Required Education: Bachelor’s degree required. Additional experience may be substituted for education.
Required Experience: Five years of related work experience with computer systems and applications.
Preferred Qualifications
Additional Qualifications • Experience leading complex projects involving emerging technologies • Familiarity with AI solution lifecycle (pilot, evaluation, scaling) • Strong facilitation and stakeholder engagement skills • Understanding of enterprise system integration and workflow design. Five years of related work experience with computer systems and applications.
Knowledge, Skills, \& Abilities (KSA’s):
- Must possess all requisite knowledge, skills, and abilities as posted in the supplemental section.
- Must demonstrate strong critical thinking and analytical reasoning skills.
- Ability to work on multiple priorities effectively.
- Ability to prioritize conflicting demands.
- Ability to work collaboratively in a team environment.
- Ability to communicate effectively in the service of users and colleagues.
- Ability to delegate tasks.
- Ability influences others and garner buy in.
- Ability to build effective teams to execute projects.
- Strong decision making skills.
- Writes and communicates clearly and concisely.
- Possesses sound documentation skills.
- Ability to maintain confidentiality.
- Must demonstrate exemplary customer service skills
- Demonstrated ability to establish and meet work schedules within limited time frames and under tight deadlines.
- Willingness to work off hours as needed in support of changes to the computing environment
- Proven success on meeting project deadlines and timelines.
- Participates in the specification, design, implementation, and management of multiple projects
- Strong technical skills.
- Strong ability to understand complex business processes.
Salary Range: Minimum 46\.66/hour \- Maximum 81\.67/hour. Compensation will be commensurate with equity and experience for roles of similar scope and responsibility. In cases where the range is displayed as a $0 amount, salary discussions will occur during candidate screening calls, before any subsequent compensation discussion is held between the candidate and any hiring authority.
The Hospital reserves the right to modify employee schedules as needed.
We are committed to creating a welcoming and inclusive environment, where we embrace and celebrate our differences, where all employees feel valued, contribute to our mission of serving the community, and engage in equitable healthcare delivery and workforce practices.
Johns Hopkins Health System and its affiliates are drug\-free workplace employers.
Johns Hopkins Health System and its affiliates are an Equal Opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity and expression, age, national origin, mental or physical disability, genetic information, veteran status, or any other status protected by federal, state, or local law.
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 Johns Hopkins Health System, 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. Mid-level AI roles across all categories have a median of $194,400.
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.
Johns Hopkins Health System AI Hiring
Johns Hopkins Health System has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Baltimore, MD, US.
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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