Summary
Design enterprise AI applications using language models, retrieval techniques, semantic search, and healthcare data standards to improve intelligent information access.
Highlights
Develop advanced AI solutions for healthcare using modern language models, retrieval systems, and interoperability standards.
Description
Our client is expanding the Provincial Health Services Directory (PHSD) with AI capabilities to improve provider discovery, referral intelligence, and healthcare navigation across provincial digital health applications.
The successful consultant will design and develop enterprise AI solutions using Retrieval-Augmented Generation (RAG), Model Context Protocol (MCP), Large Language Models (LLMs), semantic search, and healthcare interoperability standards.
The platform integrates PHSD FHIR APIs, Smile CDR, AWS HealthLake, Provider Registry, Master Data Management, and other provincial healthcare assets to deliver grounded, explainable AI responses.
Responsibilities
Design and develop enterprise AI applications using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and AI agents.
Build and optimize RAG pipelines using vector databases and semantic search.
Develop and implement Model Context Protocol (MCP) servers and AI agent workflows.
Integrate AI solutions with healthcare systems using FHIR R4/R4B, SMART on FHIR, and REST APIs.
Develop backend services using Java Spring Boot and/or Python.
Build intelligent provider search and referral recommendation capabilities.
Design prompt orchestration, tool-calling workflows, and AI evaluation frameworks.
Optimize AI performance, retrieval accuracy, grounding, and hallucination reduction.
Implement secure, scalable cloud-native solutions on AWS, including Amazon Bedrock and OpenSearch.
Produce technical documentation and collaborate with cross-functional Agile teams.
Must-Have Qualifications
Extensive experience developing enterprise AI applications.
Strong hands-on experience with Retrieval-Augmented Generation (RAG), vector search, and semantic retrieval.
Experience building AI agents using Model Context Protocol (MCP).
Experience integrating Large Language Models (LLMs) into enterprise applications.
Strong knowledge of FHIR R4/R4B, SMART on FHIR, REST APIs, and healthcare interoperability.
Experience developing backend applications using Java Spring Boot and/or Python.
Hands-on experience with Amazon Bedrock, AWS AI Services, and OpenSearch.
Experience with prompt engineering, agent orchestration, and tool calling.
Experience working with healthcare provider directories or health services directories.
Knowledge of OAuth2, OpenID Connect (OIDC), JWT, and secure API development.
Experience evaluating AI models for accuracy, grounding, and hallucination reduction.
Public sector experience is required.
Nice-to-Have Qualifications
Experience with healthcare referral, scheduling, or care navigation systems.
Experience with AWS HealthLake, Smile CDR, Provider Registry, or Master Data Management (MDM).
Experience with Amazon Bedrock Agents or other AI orchestration platforms.
Knowledge of SNOMED CT, LOINC, FHIR terminology services, and healthcare ontologies.
Experience developing healthcare knowledge graphs or semantic search platforms.
Familiarity with Responsible AI, AI governance, privacy, and explainability.
Experience integrating with Epic, Oracle Health (Cerner), MEDITECH, or similar healthcare systems.
Experience working in Agile environments delivering enterprise healthcare solutions.
