Atmosera empowers businesses to redefine what's possible with modern technology and human expertise. Our exceptional experience across Applications, Data & AI, DevOps, Security, and the Microsoft Azure platform enables organizations to accelerate innovation, enhance security, and optimize operational agility. As a Microsoft Partner with nine specializations, GitHub AI Partner of the Year, a member of the GitHub Advisory Board, and a member of the prestigious Microsoft Intelligent Security Association (MISA), Atmosera expertly delivers cutting-edge, integrated solutions that deliver business value.
We are seeking an experienced Data Engineer / AI/ML Specialist to join the development team building a next-generation AI-powered Electronic Health Record (EHR) MVP for senior and assisted living facilities. This platform will combine core EHR functionality with real-time AI capabilities—including document ingestion pipelines, predictive analytics, and natural language search/chat—running on Microsoft Azure.
The contractor will be embedded in an agile co-development team, working closely with solution architects, backend/frontend developers, and DevOps engineers, as well as the client’s technical leadership and product owner. The role will own the design and delivery of the data pipelines, AI/ML integrations, and supporting infrastructure needed to bring the MVP to life.
Key Responsibilities
- Data Engineering
- Design, implement, and optimize OCR and data extraction pipelines for ingesting PDF referral forms and other healthcare documents.
- Develop data transformation and mapping processes to store extracted data into an Azure SQL schema.
- Build and maintain the medallion data architecture (Bronze/Silver/Gold) in Azure Data Lake Storage to support both application workflows and future analytics/model training.
- Implement and optimize search indexing (Azure AI Search) for both structured and unstructured data.
- AI/ML Development
- Integrate Azure OpenAI or equivalent LLM services with retrieval-augmented generation (RAG) to support a natural language query feature.
- Implement initial predictive analytics models (fall risk, infection risk, medication cost forecasting) using heuristics or light ML models, with a path to more advanced modeling in Phase 2.
- Develop a compliance AI agent (MVP version) that runs scheduled checks on documentation and flags potential violations.
- Collaborate with the Solution Architect to ensure AI components are secure, HIPAA-compliant, and cost-optimized.
- Collaboration & Delivery
- Work in Azure DevOps with sprint-based development, pairing with backend/API developers on AI integration points.
- Participate in architecture/design reviews and provide input on data model decisions.
- Document pipelines, AI integrations, and data flows for future handoff to the client’s internal team.
- Support UAT for AI/data features, including tuning models and validating extracted data.
Required Skills & Experience
- Technical Expertise
- Proven experience with Azure Data Services (Azure SQL, Data Lake Storage, Azure AI Search, Azure Functions).
- Hands-on experience with OCR (Azure Form Recognizer / Azure Document Intelligence) and unstructured data processing.
- Strong Python skills for AI pipeline and data processing development.
- Experience integrating LLMs (Azure OpenAI) with RAG patterns.
- Knowledge of basic predictive modeling techniques (classification, regression) and healthcare data compliance considerations.
- Proficiency with REST APIs for integrating external systems (e.g., pharmacy, insurance).
- Healthcare & Compliance
- Understanding of HIPAA compliance and secure handling of PHI.
- Familiarity with healthcare data formats and workflows (HL7, FHIR, eMAR).
- Collaboration & Delivery
- Prior experience working in agile, cross-functional product teams.
- Ability to deliver end-to-end—from data ingestion to model integration to API exposure.
- Strong documentation and knowledge transfer skills.
Preferred Qualifications
- Experience in healthcare software development (EHR, EMR, or related domains).
- Familiarity with vector search and semantic search concepts.
- Experience with Azure Machine Learning for model deployment.
- Background in predictive analytics for healthcare outcomes.