Interoperability Architect - Health Care and Life Sciences
Accenture
Interoperability Architect - Health Care and Life Sciences
Job Description
We Are:
Applied Intelligence, the people who love using data to tell a story. We’re also the world’s largest team of data scientists, data engineers, and experts in machine learning and AI. A great day for us? Solving big problems using the latest tech, serious brain power, and deep knowledge of just about every industry. We believe a mix of data, analytics, automation, and responsible AI can do almost anything—spark digital metamorphoses, widen the range of what humans can do, and breathe life into smart products and services. Want to join our crew of sharp analytical minds? Visit us here to find out more about Applied Intelligence.
We work at the intersection of technology, business, and health, helping clients in healthcare and life sciences solve critical problems through data interoperability, AI-driven automation, and intelligent system design.
You Are:
An experienced architect who thrives at the crossroads of healthcare interoperability and emerging AI technologies. You have hands-on experience in designing data solutions, with the curiosity and adaptability to deepen your expertise in AI tools, models, and platforms.
You're excited to help clients navigate change, not just implement technology. You bring technical depth, strategic thinking, and a collaborative spirit, and you’re motivated to build systems that deliver real-world value through interoperability and AI innovation.
What You’ll Do:
Lead and support consulting engagements that tackle complex interoperability and AI challenges in healthcare and life sciences.
Provide hands-on architectural leadership and act as a bridge between business and technology, helping clients translate goals into solution architectures and roadmaps.
Architect and prototype data exchange and interoperability solutions, with an emphasis on scalability in cloud environments (e.g., Azure, AWS, GCP).
Collaborate on the design and implementation of AI-powered solutions, using emerging tools and platforms (e.g., LLMs, automated coding assistants, AI APIs).
Lead small teams to develop innovative solutions for clients across functions & industries
Stay abreast of data-exchange/interoperability and AI technologies, academic research & hands-on techniques.
Qualifications
What We’re Looking For:
Minimum 5 years implementing data exchange/interoperability solutions in healthcare or life sciences - ideally in cloud environments (e.g., Azure, AWS, GCP).
Minimum 5 years in solution or enterprise architecture roles, with the ability to design conceptual, logical, and physical architectures.
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Minimum 1–2 years of experience designing or integrating AI/ML tools or capabilities into technical solutions—such as:
Architecting systems that incorporate AI services or models (e.g., for clinical NLP, image analysis, or predictive analytics)
Designing end-to-end data pipelines that support AI/ML use cases in healthcare or life sciences
Selecting appropriate AI platforms, services, or tools (e.g., Azure AI, AWS SageMaker, Vertex AI) as part of broader solution architecture
Collaborating with data scientists, ML engineers, or research teams to ensure AI solutions are scalable, compliant, and interoperable
Experience with clinical data models as well as data exchange standards and frameworks (e.g., HL7 FHIR, v2, CDA, IHE, etc.)
Client-facing experience in consulting or solution design settings, with the ability to translate business requirements into technical architecture.
Familiarity with AI solution architecture principles —such as integrating AI models into data pipelines, using vector databases, or managing model lifecycle.
Understanding of or experience with responsible AI concepts (e.g., fairness, explainability, bias mitigation) as they apply to healthcare or life sciences.
Minimum undergraduate degree from a recognized university in an applicable field such as Bachelor of Science, Computer Science or Health Informatics.
Nice to have:
Expertise in using Hadoop, MapReduce, Spark, Pig, Hive technologies, Data Ingestion in Data Lake, Data Storage on key Cloud providers (AWS, Azure, GCP), Redshift, Data Retrieval on Big Data platforms and Interfacing Data Science and Data Visualization tools on Big Data platforms
Experience in Data Modeling, Big Data Platforms (e.g. Cloudera, Hortonworks, AWS, Talend, etc.), Data Migration and Quality (using ETL e.g Informatica), MySQL/NoSQL, CQRS Event Sourcing
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