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About This Role
It's fun to work in a company where people truly BELIEVE in what they're doing!
*We're committed to bringing passion and customer focus to the business.*
Summary
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The AI \& Analytics Engineer I supports the design, build, and delivery of AI\-powered applications, data pipelines, and analytics solutions that drive operational efficiency and decision\-making across the organization.
This is a developing\-professional role focused on hands\-on execution under the guidance of senior engineers, with the goal of expanding the team’s capacity to deliver AI applications and distribute insights at greater velocity.Description
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Core Responsibilities
AI Applications Development
- Assist in building and enhancing AI\-powered applications and agents that support business workflows
- Develop components of AI solutions that automate routine tasks and surface insights
- Gather requirements from stakeholders with guidance from senior team members
- Iterate on AI applications based on user feedback and testing results
- Provide ongoing Level 3 support for software products
AI Integration \& Delivery
- Support integration of AI applications with enterprise systems (e.g., EMR, HRIS, data platforms) under senior direction
- Assist with deployment, testing, and monitoring of AI solutions in lower and production environments
- Translate documented business requirements into functional workflows
- Follow established standards for reliability, security, and code quality
Data Engineering \& Pipeline Development
- Build and maintain ETL/ELT pipelines that feed analytics and AI use cases
- Ingest and transform data from multiple source systems into centralized platforms
- Validate accuracy, completeness, and structure of pipeline outputs
Analytics \& Data Modeling
- Develop and maintain semantic models, datasets, and dashboards (Power BI and related tools)
- Apply standardized business metrics and KPI definitions across reports
- Optimize queries and data structures for performance and usability
- Implement and maintain row\-level security and access controls on reports
Cross\-Functional Collaboration
- Partner with IT, clinical informatics, operations, and business stakeholders to understand reporting and AI needs
- Communicate progress, blockers, and trade\-offs clearly to both technical and non\-technical audiences
- Escalate architectural or scope questions to senior engineers
EngineeringStandards \& Quality
- Follow team practices for source control, code review, documentation, and testing
- Monitor AI outputs and data pipelines for accuracy and reliability
- Support compliance with data security, privacy, and governance standards (HIPAA\-aware)
Continuous Learning \& Improvement
- Build technical depth in AI/ML tooling, cloud services, and modern data platforms
- Contribute small improvements to existing AI tools, dashboards, and pipelines
- Stay current with emerging AI and analytics technologies relevant to healthcare operations
Scope \& Impact
- Contributes directly to AI application and analytics delivery used across business operations
- Expands team throughput on AI applications, dashboards, and insight distribution
- Operates under the technical direction of the AI \& Analytics Engineer II and Senior Director of AI, Data, \& Enterprise Applications
Success Metrics
- Volume and quality of analytics and AI deliverables completed
- Reliability and performance of owned pipelines and reports
- Reduction in backlog for analytics and AI requests
- Growth in technical proficiency over the first 12–18 months
- Stakeholder satisfaction with delivered solutions
Target Compensation: 125k \- 135k
The salary/rate range listed here has been provided to comply with local regulations and represents a potential base salary/rate for this role. Please note that actual salaries/rates may vary within this range above or below, depending on experience and location. We look at compensation for each individual and based on experience and qualifications.
Qualifications
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Qualifications
Experience
- 2–4 years of experience in data analytics, BI development, data engineering, or software development
- Hands\-on experience building custom software products, including writing code and delivering end\-to\-end solutions aligned to business requirements.
- Proven ability to conduct testing, troubleshoot and debug issues, and ensure high\-quality, reliable application performance across environments.
- Hands\-on experience building dashboards, reports, or data pipelines in a professional setting
- Exposure to AI, machine learning, or workflow automation projects (academic or professional) preferred
Technical \& Functional Expertise
- Demonstrated proficiency in modern software engineering tools (IDES), practices; including Agile development, CI/CD workflows, automated testing, and code review standards.
- Working proficiency with SQL and relational data modeling
- Experience with Power BI (or comparable BI platform) including DAX and semantic models
- Familiarity with ETL/ELT concepts and at least one data integration tool
- Exposure to cloud platforms (Azure preferred) and AI/ML services a plus
- Comfort working with both structured and unstructured data
Competencies
- Strong analytical and problem\-solving skills with attention to detail
- Willingness to learn from senior engineers and apply feedback quickly
- Effective written and verbal communication with technical and non\-technical partners
- Ability to manage multiple concurrent tasks and meet committed delivery dates
- Healthcare or pediatrics domain interest is a plus
Compensation:
Role Dependent
The salary/rate range listed here has been provided to comply with local regulations and represents a potential base salary/rate for this role. Please note that actual salaries/rates may vary within this range above or below, depending on experience and location. We look at compensation for each individual and based on experience and qualifications.
EEO Statement
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PM Pediatric Care is an equal employment opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, sexual orientation, gender identity or expression, national origin, disability status, protected veteran status or any other characteristic protected by law.
Salary Context
This $125K-$135K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 2130 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 4,133 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At PM Pediatrics Management Group, 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 $185,000 based on 13,200 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,778. This role's midpoint ($130K) sits 30% below the category median. Disclosed range: $125K to $135K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
PM Pediatrics Management Group AI Hiring
PM Pediatrics Management Group has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Lake Success, NY, US. Compensation range: $135K - $135K.
Location Context
Across all AI roles, 14% (583 positions) offer remote work, while 3,532 require on-site attendance. Top AI hiring metros: New York (2,760 roles, $211,000 median); San Francisco (2,258 roles, $253,000 median); Los Angeles (1,841 roles, $195,000 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 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 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 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). 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 (106) are outnumbered by mid-level (1,901) and senior (1,663) 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 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 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 $200,700. Top-quartile roles start at $254,000, 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 Safety roles lead at $274,200 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 (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 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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