Within Data Ingestion at Aledade we have two major problems: 1) Scale, 2) Variability. As an Ingestion AI Engineer at Aledade, you will be focused on the variability problem. Leveraging cutting edge AI tools you will design systems that handle the variability of data received from external sources. Within healthcare, while there are format standards, they are loosely followed and in many cases for the data we receive, no standard exists. Handling hundreds/thousands of disparate formats quickly is a challenge. The faster we are able to stand up new data feeds, the more information we have about our patient population, the better care we enable our providers to render. We're looking for engineers who know that writing new code is not always the solution to a problem, but when technological changes are needed they create secure, maintainable, performant, correct, scalable, and stable solutions to the complex and unique challenges in our corner of the healthcare industry.

They embrace strategies that minimize risk, leaning towards observability, alerting, metrics, high test coverage, and frequent releases that incrementally build value.

• Develop and implement scalable and performant solutions.
• Partner, as a peer, with Engineering Managers, Product Managers, and stakeholders throughout Aledade to develop and execute technical roadmaps using Agile processes.
• Mentor and coach more junior engineers including thorough pull request reviews for other developers and be receptive to critical feedback on your own work.

• Experience with health-tech systems, like Electronic Health Records, Clinical data, etc.
• Experience in designing, building and optimizing data pipelines and ETL processes.
• Proficiency in working with large datasets and knowledge of data storage technologies.
• Experience working with data ingestion systems and optimizing performance for handling large-scale data processing and analysis.
• In-depth knowledge of database systems.
• Experience in performance monitoring and optimization of data systems and infrastructure.
• Experience with containerization and orchestration technologies such as Docker and Kubernetes.
• Experience building continuous integration and continuous deployment(CI/CD) pipelines.
• Experience with security and systems that handle sensitive data.
• Expertise with statistical data techniques (such as causal inference, syntactic analysis, sampling methods, NLP etc), with experience in addressing challenges from incomplete, unrepresentative, and mislabeled data.
• 1+ years of experience building systems incorporate AI SDK access.
• Experience with prompt engineering, tool calling, MCP development, vector databases, or RAG.
• AI system design.
• Experience navigating considerations and tradeoffs regarding determinism vs LLM in the loop .
• Development of applications using foundational models.
• Experience with cloud computing platforms such as AWS, Azure or Google Cloud.