Senior Generative AI Lead

Senior Generative AI Lead

8 to 10 Years Canada All Jobs

We are seeking a highly hands‑on Senior GenAI Lead with 8+ years of experience in AI/ML engineering and enterprise system delivery. This role combines deep machine learning expertise, modern Generative AI architecture, and strong technical leadership to build scalable, production‑ready AI platforms. The ideal candidate brings strong foundations in classical machine learning, predictive modeling, and data science, combined with hands‑on experience in LLM orchestration, multi‑agent systems, and enterprise AI modernization.

This is a technical leadership role focused on architecture, execution, engineering rigor, and mentorship.

• 8+ years of experience in AI/ML engineering, data science, and enterprise software systems
• 3+ years building production‑grade Generative AI and LLM‑based systems
• Proven experience delivering ML and AI solutions in one or more domains like manufacturing, retail, healthcare, logistics, or enterprise domains.

• Architect and implement enterprise‑grade GenAI platforms using LLMs and multi‑agent orchestration frameworks (Langgraph, CrewAI, Agent2
  
  Agent, MCP, etc.)
• Design scalable RAG architectures using vector databases and structured knowledge systems
• Build hybrid AI systems integrating predictive ML models with GenAI copilots
• Lead cloud‑native AI deployments across Azure, AWS, and GCP

• Design, develop, and deploy classical ML models including regression, classification, forecasting, churn prediction, anomaly detection, and optimization
• Perform EDA, feature engineering, model evaluation, and productionisation
• Implement model monitoring, validation frameworks, and retraining strategies
• Integrate ML models into GenAI workflows for decision intelligence
• Apply statistical rigor and business KPIs to measure model impact

• Develop Python‑based AI systems with strong coding standards
• Build and review agentic AI workflows and modernization automation frameworks
• Implement test‑driven validation pipelines and data validation systems
• Ensure scalability, resilience, and cost‑efficient AI deployments
• Own end‑to‑end delivery from architecture to production rollout

• Integrate Responsible AI features such as prompt shields, groundedness detection, and risk monitoring
• Design governance frameworks for enterprise AI deployments
• Ensure compliance with enterprise IT and security standards

• Mentor AI engineers and data scientists in multi‑agent architecture and ML best practices
• Conduct code reviews and architecture reviews
• Promote reproducibility, testing discipline, and failure‑mode design

• LLM orchestration frameworks (CrewAI, Auto Gen, Lang Graph)
• RAG architecture design
• Vector databases
• Prompt engineering & context management

• Supervised & unsupervised learning
• Forecasting & time‑series modeling
• Classification & regression modeling
  
  Feature engineering & model validation
• ML pipeline design & monitoring
• Statistical evaluation techniques

• Strong Python programming
• Azure / AWS / GCP experience
• API‑based integration of AI systems
• Production deployment and CI/CD workflows

• Production‑grade AI systems deployed with measurable business impact
• Reliable multi‑agent orchestration pipelines with validation and governance layers
• ML models that are statistically sound and business‑aligned
• Teams enabled to independently extend AI systems using strong architecture patterns
• AI platforms built with resilience, monitoring, and explicit failure handling