Specialist, AI Solutions

Key Responsibilities

• AI Solution Design & Development
• Design and build production‑grade AI solutions leveraging Large Language Models (LLMs), Context Engineering, Retrieval‑Augmented Generation (RAG), and classical machine‑learning techniques
• Define Agent design development standards and orchestration Design Patterns
• Develop reusable prompt, context, and feature engineering patterns to improve model accuracy, grounding, and performance.
• Integrate AI capabilities into enterprise applications using REST and Graph
  
  QL APIs, OpenAPI/Swagger specifications, and Azure‑native services.
• Use AI‑assisted development tools (Git Hub Copilot, Cursor, Claude Code) to accelerate delivery while maintaining code quality and review standards.
• Design & Build AI Solutions:
  Develop and deploy AI applications on Microsoft Copilot, Azure AI Foundry, Azure App Services.
• API & Service Integration:
  Connect Azure AI services (Vision, Speech, Language, Document Intelligence) to existing applications using REST APIs and Python/C# SDKs.
• RAG & Knowledge Mining:
  Build Retrieval‑Augmented Generation (RAG) pipelines with Azure AI Search to ground AI responses in enterprise data.
• Responsible AI Guardrails:
  Apply content safety, prompt shields, and PII filters via Azure AI Content Safety to ensure compliant AI deployments.
• Build and maintain data ingestion, transformation, and curation pipelines using Microsoft Fabric Pipelines, Fabric Notebooks (PySpark), and Delta Tables.
• Utilize Lakehouse and Data Warehouse patterns following the standards prepared by Data Engineering teams.
• Prepare the Context of AI Agents through the use of data preparation and data pipeline orchestration.
• Prepare high‑quality data for RAG, embeddings, and vector search through profiling, cleansing, validation, and deduplication.
• Building interactive UI components in React/Next.js/JavaScript for AI applications and dashboards – utilizing AI coding assistance.
• Application Insights, telemetry, alerting, and performance monitoring.
• MLOps & Dev Ops:
  Build CI/CD pipelines in Git Hub Actions or Azure Dev Ops to register, test, and deploy models.
• Collaborative communication with engineering, analytics, architecture, and business stakeholders.

• AI & Machine Learning:
  Hands‑on with Large Language Models, prompt and context engineering, RAG, feature engineering, ML model development, and AI‑assisted coding tools (Git Hub Copilot, Cursor, Claude Code).
• Azure & Cloud AI Services:
  Azure infrastructure and cloud‑based AI service deployment, plus working knowledge of Azure Container Apps, App Services, and Azure Dev Ops pipelines.
• Microsoft Fabric:
  Fabric Pipelines, Delta Tables, and Fabric Notebooks (PySpark);
  Lakehouse and Data Warehouse patterns; event‑driven (RTI) architecture.
• Data Engineering:
  Building data ingestion pipelines, data curation with SQL and Python, and data cleansing/deduplication;
  Fabric Notebook.
• Core Development:
  Python for AI/ML and automation;
  Type Script, React/Next.js, Node.js for full‑stack and dashboards;
  PySpark for distributed processing.
• APIs & Backend: REST and Graph
  
  QL API development with OpenAPI/Swagger specifications.
• Analytics & Reporting:
  Power BI report design, DAX, semantic models, and Fabric Direct Lake integration; conceptual, logical, and physical data modeling.
• Dev Ops & Version Control:
  Advanced Git source control and branching strategies; CI/CD, containerization, and infrastructure‑as‑code practices.
• Observability:
  Application Insights, telemetry, custom metrics, distributed tracing, alerting, and Azure observability platforms.

• Bachelor’s degree in Computer Science, Information Systems, Engineering, or equivalent professional experience.
• Nice to have relevant Microsoft certifications preferred:
  Azure AI Engineer Associate (AI‑102), Azure Data Engineer Associate (DP‑203), and Fabric Analytics Engineer Associate (DP‑600).
• 3+ years of hands‑on experience building production software, with at least 2 years focused on AI solutions and cloud‑native data solutions.

The base salary range for this position is $ 90,080– $123,860 per year (CAD). The posted range reflects the expected base salary for this position. Actual base salary will be determined based on factors such as geographic location, skills, education, and experience, as well as internal equity considerations. Offers are typically made within the range and not at the top of the range to support growth and progression.

This position is eligible to participate in a short‑term incentive plan. Incentive awards, if any, are based on business and individual performance and are not guaranteed.