AI & Analytics Engineer

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Summary
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

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