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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 user\-facing AI\-powered applications (including frontend interfaces and backend services), 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
- Build user\-facing interfaces using a modern frontend framework (React, Vue, Angular, or similar) that surface AI capabilities to clinical and operational users
- Develop backend services and REST or GraphQL APIs (Python, Node.js, .NET, or similar) that integrate AI capabilities into 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
Engineering Standards \& 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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- 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
- Hands\-on experience building and shipping user\-facing applications end\-to\-end, including both frontend (web or mobile UI) and backend (services, APIs, data access) components
- Demonstrated proficiency with a modern frontend framework (React, Vue, Angular, or similar)
- Demonstrated proficiency with a backend framework (FastAPI, Django, Express, ASP.NET Core, Spring Boot, or similar)
- Active GitHub profile or equivalent code portfolio with reviewable code samples (required at application)
- 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.
- Daily experience with Git and GitHub (or comparable), including branching, pull requests, and code review workflows
- 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 Software Engineer roles in our dataset (median: $185K across 231 roles with salary data).
Role Details
About This Role
AI Software Engineers build the applications and systems that AI models run inside. They own the API layers, data pipelines, frontend integrations, and infrastructure that turn a model into a product users interact with. Every AI company needs engineers who can build the software around the AI.
The challenge is building reliable systems around inherently unreliable components. Models are probabilistic. They'll give different answers to the same question. They hallucinate. They're slow. They're expensive. Your job is to build an application layer that handles all of this gracefully while delivering a product that users trust and enjoy.
Across the 4,317 AI roles we're tracking, AI Software Engineer positions make up 7% of the market. At PM Pediatrics Management Group, this role fits into their broader AI and engineering organization.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
What the Work Looks Like
A typical week includes: building API endpoints that serve model inference with caching and fallback logic, designing the data pipeline that feeds context to a RAG system, implementing streaming responses in the frontend, debugging a race condition in the async inference pipeline, and optimizing database queries for the vector search layer. It's full-stack engineering with AI at the center.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
Skills Required
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
Knowledge of vector databases, embedding APIs, and LLM integration patterns (function calling, structured outputs, retry logic) differentiates AI software engineers from general software engineers. Understanding cost optimization (caching strategies, model routing, batched inference) is valuable since inference costs can dominate application economics.
Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
Compensation Benchmarks
AI Software Engineer roles pay a median of $218,500 based on 729 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($130K) sits 41% below the category median. Disclosed range: $125K to $135K.
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.
PM Pediatrics Management Group AI Hiring
PM Pediatrics Management Group has 1 open AI role right now. They're hiring across AI Software Engineer. Based in Lake Success, NY, US. Compensation range: $135K - $135K.
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 Software Engineer roles include Software Engineer, Full-Stack Developer, Backend Engineer.
From here, career progression typically leads toward Staff Engineer, AI Architect, Engineering Manager.
If you're a software engineer, you're already 80% there. Learn the AI integration patterns: RAG, streaming inference, function calling, structured outputs. Build a project that demonstrates you can wrap an AI model in a production-quality application with proper error handling, caching, and user experience. That's the portfolio piece that gets you hired.
What to Expect in Interviews
Technical screens look like standard software engineering interviews with an AI twist. Expect system design questions about building reliable applications around probabilistic models: handling streaming responses, implementing retry logic for API failures, and designing caching strategies for LLM outputs. Coding rounds test standard algorithms plus practical integration patterns like async processing and rate limiting.
When evaluating opportunities: Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
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).
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
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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