Interoperability Architect - Health Care and Life Sciences

Accenture•Toronto, ON

3d•$36 - $60•Onsite

About The Position

We are Applied Intelligence, a global team of data scientists, data engineers, and experts in machine learning and AI, focused on solving big problems using the latest technology and deep industry knowledge. 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. We are seeking an experienced architect who thrives at the crossroads of healthcare interoperability and emerging AI technologies. This role requires hands-on experience in designing data solutions, with the curiosity and adaptability to deepen expertise in AI tools, models, and platforms. The ideal candidate is excited to help clients navigate change, not just implement technology, bringing technical depth, strategic thinking, and a collaborative spirit to build systems that deliver real-world value through interoperability and AI innovation.

Requirements

Minimum 3+ years implementing data exchange/interoperability solutions in healthcare or life sciences - ideally in cloud environments (e.g., Azure, AWS, GCP).
Minimum 3+ years in solution or enterprise architecture roles, with the ability to design conceptual, logical, and physical architectures.
Minimum 1–2 years of experience designing or integrating AI/ML tools or capabilities into technical solutions.
Experience architecting systems that incorporate AI services or models (e.g., for clinical NLP, image analysis, or predictive analytics).
Experience designing end-to-end data pipelines that support AI/ML use cases in healthcare or life sciences.
Experience selecting appropriate AI platforms, services, or tools (e.g., Azure AI, AWS SageMaker, Vertex AI) as part of broader solution architecture.
Experience 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 Haves

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.

Responsibilities

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.