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About This Role
The AI Engineer is responsible for designing, developing, and deploying AI\-driven automation solutions across PAM Health. Acting as a crucial technical contributor, this individual will partner with business stakeholders across the organization to identify workflows that can be optimized. Leveraging large language models (LLMs) and agentic frameworks, the AI Engineer will build tools that enhance organizational efficiency and support patient care. This role spans a broad range of experience levels (Junior to Senior, with expectations scaling accordingly) and requires a strong commitment to deploying technology within a secure, HIPAA\-compliant healthcare environment. I. Position Responsibilities* AI Development \& Automation: Design, build, and maintain AI agents and automation workflows using Python to streamline processes across clinical, financial, HR, and administrative departments
- Framework Utilization: Leverage modern agent development SDKs and frameworks (such as LangChain, Pydantic AI, Google ADK, or the OpenAI Agents SDK) to build robust, scalable AI applications
- System Integration: Integrate AI solutions with existing enterprise applications and Electronic Health Record (EHR) systems, facilitating secure and efficient data exchange
- Cloud Deployment: Deploy, host, and manage AI models and applications within cloud infrastructure
- AI\-Assisted Engineering: Accelerate development timelines and maintain high code quality by actively utilizing AI coding assistants (e.g., Claude Code, Cursor, or Antigravity)
- Security \& Compliance: Ensure all AI tools and automated workflows adhere to strict healthcare compliance standards, maintaining data privacy, security, and HIPAA compliance when handling Protected Health Information (PHI)
- Cross\-Functional Collaboration: Partner with non\-technical stakeholders across the organization to gather requirements, scope automation projects, and translate business challenges into actionable AI solutions
- Continuous Innovation: Actively monitor, test, and evaluate emerging AI technologies, models, and industry best practices
II. Leadership* Inclusiveness: Promotes cooperation, fairness and equity; shows respect for people and their differences; works to understand perspectives of others; demonstrates empathy; brings out the best in others and in his/her team
- Managing Staff: Coaches, evaluates, develops, and inspires staff; sets expectations; recognizes achievements
- Stewardship and Resource Management: Demonstrates accountability and sound judgment in managing company resources; appropriate understanding of confidentiality and company values; adheres to and supports company policies, procedures and safety guidelines
- Problem\-Solving: Identifies problems and involves others in seeking solutions; conducts appropriate analysis and searches for best solutions; effectively and efficiently implements appropriate responses to correct problems; responds promptly and effectively to new challenges
- Decision\-Making: Makes clear, consistent decisions; acts with integrity in all decisions; distinguishes relevant from irrelevant information; makes timely, appropriate decisions.
- Strategic Planning and Organizing: Understands company vision and aligns priorities accordingly; measures outcomes; uses feedback to redirect as required; evaluates alternatives; appropriately organizes complex issues to desirable resolution
- Communication: Connects with peers, subordinate employees and all customers; actively listens; clearly and effectively shares information; demonstrates effective oral and written communication skills; negotiates effectively.
- Quality Improvement: Strives for efficient, effective, high\-quality performance in self and in the department; delivers timely and accurate results; resilient when responding to matters that are challenging; takes initiative to make improvements
- Leadership: Motivates others; accepts responsibility; maintains high morale in department; develops trust and credibility; expects honest and ethical behavior of self and staff
- Teamwork: Encourages cooperation and collaboration; builds effective teams; works in partnership with others; is flexible; responsive to the needs of others
- Development: Maintains up\-to\-date skills through involvement with professional organizations and/or continuing education
III. Customer Service* Maintains the highest level of customer service via courtesy, compassion and positive communication.
- Promotes the mission and vision of PAM Health within the work environment and the community.
- Respects dignity and confidentiality by adherence to all applicable policies and procedures.
IV. Health and Safety* Works in a manner that promotes safety; wears clothing appropriate to the performance of the job.
- Participates in OSHA required training.
- Follows universal precautions as appropriate for position; complies with Employee Health requirements for continued employment.
- Reports unsafe practices to management.
- Knows own role in case of an emergency
POSITION QUALIFICATIONS: Education and Training:
Bachelor’s degree in Computer Science, Software Engineering, Data Science, Artificial Intelligence, or a related technical field; OR equivalent practical/professional experience. Experience:* Proven software engineering experience (level commensurate with Junior to Senior expectations), with a primary focus on Python development
- Demonstrated hands\-on experience building, testing, and deploying AI\-driven applications, LLMs, or autonomous aagents
- Preferred: Familiarity with the healthcare industry; especially in the post\-acute care sector
- Preferred: Experience integrating with, or extracting data from, Electronic Health Record (EHR) systems
- Preferred: Experience deploying AI and software solutions in secure, HIPAA\-compliant environments handling sensitive data
- Preferred: Experience with cloud computing platforms, specifically Microsoft Azure and/or Google Cloud Platform (GCP)
Knowledge, Skills, and Abilities:* Strong proficiency in Python programming and its broader data/AI ecosystem
- Familiarity with agent development SDKs (e.g., LangChain, Pydantic AI, Google ADK, OpenAI Agents SDK)
- High proficiency with modern AI coding assistants (e.g., Claude Code, Cursor, Antigravity)
- Solid understanding of Retrieval\-Augmented Generation (RAG) fundamentals (embeddings, vector stores, multi\-modal data processing
- Strong understanding of RESTful API development and third\-party system integrations
- Knowledge of data privacy principles, security best practices, and healthcare regulatory requirements (HIPAA)
- Excellent problem\-solving skills with the ability to operate as a technical generalist across varied business domains
- Strong communication skills, with the ability to explain complex AI concepts to non\-technical stakeholders clearly and effectively
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 PAM Health, 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 $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.
PAM Health AI Hiring
PAM Health has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Plano, TX, 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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