Build and enhance user-facing AI applications, frontend interfaces, backend services, APIs, data pipelines, semantic models, dashboards, and analytics solutions. Integrate AI tools with enterprise systems, support deployment and monitoring, validate pipeline and AI output quality, implement reporting security controls, and provide Level 3 product support. Collaborate with technical, clinical, operational, and business stakeholders while following software engineering, testing, security, privacy, and governance standards.
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.
SummaryThe 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
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.
- 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 DependentThe 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 StatementPM 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.
Similar Jobs
Aerospace • Information Technology • Software • Cybersecurity • Design • Defense • Manufacturing
Lead quality engineering for illumination products: perform internal and AS9100/customer audits, drive corrective/preventive actions, analyze nonconformance trends, develop quality metrics and certification plans, and ensure design and production readiness and compliance.
Top Skills:
As9100Faa Part 145Non-Conformance (Ncr) ProcessesQmsRoot Cause Analysis (Rcca)
Aerospace • Information Technology • Software • Cybersecurity • Design • Defense • Manufacturing
Operate and maintain Insitu unmanned aircraft systems, plan and execute missions, collect/analyze payload and video data, perform pre/post-flight checks and maintenance, support customers in field deployments, and complete inventory and mission reporting. Deployable to remote/austere locations up to 70% travel.
Top Skills:
Basic NetworkingIntegratorRq21ScaneagleUas
Fintech • Information Technology • Insurance • Financial Services • Big Data Analytics
Provides financial support for group insurance clients through billing management, financial reporting, data analysis, reconciliations, revenue projections, audits, and data integrity oversight. Partners with internal and external stakeholders on implementations, renewals, underwriting, and client presentations. Documents and improves financial processes, supports compliance requirements, monitors financial trends and metrics, and contributes to cross-functional initiatives that improve operational effectiveness.
Top Skills:
Ai-Enabled ToolsExcelMS Office
What you need to know about the San Francisco Tech Scene
San Francisco and the surrounding Bay Area attracts more startup funding than any other region in the world. Home to Stanford University and UC Berkeley, leading VC firms and several of the world’s most valuable companies, the Bay Area is the place to go for anyone looking to make it big in the tech industry. That said, San Francisco has a lot to offer beyond technology thanks to a thriving art and music scene, excellent food and a short drive to several of the country’s most beautiful recreational areas.
Key Facts About San Francisco Tech
- Number of Tech Workers: 365,500; 13.9% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Google, Apple, Salesforce, Meta
- Key Industries: Artificial intelligence, cloud computing, fintech, consumer technology, software
- Funding Landscape: $50.5 billion in venture capital funding in 2024 (Pitchbook)
- Notable Investors: Sequoia Capital, Andreessen Horowitz, Bessemer Venture Partners, Greylock Partners, Khosla Ventures, Kleiner Perkins
- Research Centers and Universities: Stanford University; University of California, Berkeley; University of San Francisco; Santa Clara University; Ames Research Center; Center for AI Safety; California Institute for Regenerative Medicine
