Software Dev. Sr. Specialist Advisor
NTT Data View all jobs
- Toronto, ON
- Permanent
- Full-time
- Design and implement agent-based AI workflows for different domains such as :
- healthcare use cases (clinical assistants, triage agents, care pathway optimization, RCM automation)
- Design agentic AI architectures for manufacturing workflows
- Build multi-agent financial AI systems (analysis agents, compliance agents, advisory agents)
- Implement RAG over financial documents, policies, contracts, and filings
- Build LLM-powered systems (RAG, tool-calling agents, multi-agent orchestration)
- Develop classical ML models (risk scoring, prediction, clustering, anomaly detection)
- Implement HIPAA aware AI architectures with auditability and traceability
- Implement GenAI systems using RAG over CRM, customer data, and content
- Build full-stack applications (Python APIs, AI services, UI dashboards)
- Integrate with EHRs, data lakes, and healthcare systems (FHIR/HL7 exposure preferred)
- Integrate LLMs with sensor data, MES, ERP, and IoT platforms
- Collaborate with SMEs to translate medical workflows into agent logic
- Deploy, monitor, and optimize AI systems in Azure
- 8+ years’ experience using Python-based AI systems
- 8+ years hands-on experience with GenAI (LLMs, RAG, embeddings, prompt engineering)
- Experience with traditional ML (classification, regression, NLP, time-series)
- Full-stack experience (API design + UI integration) - 5+ Years
- Azure cloud experience (Azure ML, Azure OpenAI, Functions, AKS) - 5+ Years
- Experience working in regulated or compliance-heavy domains
- Comfortable working with subject-matter experts
- Strong learning mindset and adaptability
- Experience building production AI systems, not just prototypes
- Ability to explain AI decisions to non-technical stakeholders
- Interest in agentic AI and next-generation AI architectures
- Degree: Bachelors in Computer Science or equivalent work experience
- Multiple Domain exposure
- AWS, GCP, or NVIDIA AI stack experience
- Knowledge of model governance and explainability
- Experience with document intelligence pipelines