Digital Solution Architect Lead Advisor
CanadaFull-TimeLead
Salary80 - 90 USD per hour
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Job Details
- Experience
- 8–12 years
- Required Skills
- PythonFull Stack DevelopmentMachine LearningGenerative AIHIPAA
Requirements
- 8–12 years of experience in AI/ML engineering, software development, or related technical roles.
- Strong expertise in Python for building production-grade AI systems.
- Hands-on experience with Generative AI technologies including LLMs, embeddings, prompt engineering, and RAG architectures.
- Solid background in classical machine learning techniques such as classification, regression, NLP, and anomaly detection.
- Full-stack development experience including API design and UI integration.
- Strong experience with Azure cloud services (Azure AI Studio, Azure ML, Azure OpenAI, Functions, AKS).
- Experience working in regulated or compliance-heavy environments.
- Ability to collaborate effectively with non-technical stakeholders and domain experts.
- Proven experience delivering production-ready AI systems beyond prototypes.
- Strong communication skills with the ability to explain AI systems and decisions clearly.
Responsibilities
- Design and implement agent-based AI workflows for healthcare use cases such as clinical assistants, triage systems, care optimization agents, and revenue cycle automation.
- Develop and deploy LLM-powered systems including RAG pipelines, tool-calling agents, and multi-agent orchestration frameworks.
- Build and maintain classical machine learning models for prediction, risk scoring, clustering, anomaly detection, and time-series analysis.
- Architect and implement HIPAA-aware, secure, and auditable AI systems with strong traceability and governance.
- Develop full-stack applications including Python-based APIs, backend AI services, and interactive dashboards.
- Integrate AI systems with healthcare platforms such as EHRs, data lakes, and FHIR/HL7-based infrastructures where applicable.
- Collaborate with clinical subject-matter experts to translate healthcare processes into scalable AI agent logic and workflows.
- Deploy, monitor, and continuously optimize AI systems within Azure cloud environments.
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