Position: AI engineer/Architect
Location: Toronto, Canada(Remote)
Type : Contract
Job Description-
We are seeking a highly experienced Forward Deployed Engineer - AI Strategist to lead the endto-end adoption, deployment, and scaling of AI solutions across enterprise environments. This
role combines AI strategy, solution architecture, and deployment leadership, working closely
with business stakeholders to drive measurable impact across domains such as manufacturing,
supply chain, and operations.
You will act as the technical owner and strategic advisor, ensuring AI initiatives align with business
objectives and deliver tangible ROI.
Key Responsibilities
- AI Strategy & Business Alignment
Define and execute the enterprise AI roadmap aligned with organizational goals.
Identify high-impact use cases across manufacturing, supply chain, agriculture, and
commercial operations.
Evaluate opportunities for process optimization, automation, and decision intelligence
using AI.
Drive adoption of modern AI technologies including LLMs, AI agents, and advanced
analytics.
Architect End-to-end AI Systems, Including
- Data pipelines
- Feature/embedding pipelines
- LLM integrations and AI agents
- Real-time inference services
Design scalable systems using cloud platforms (Azure preferred, AWS/GCP).
Make trade-offs for latency, scalability, cost, and security.
Define microservices architecture using Docker & Kubernetes.
- Forward Deployment & Delivery
Lead deployment of AI solutions onsite or remotely as the technical owner.
Drive POCs, pilot programs, and MVP-to-production transitions.
Collaborate with DevOps, security, and enterprise IT teams for seamless integration.
Conduct demos, training sessions, and knowledge transfer workshops.
- Stakeholder & Leadership Engagement
Act as a bridge between business stakeholders and engineering teams.
Partner with leadership to prioritize AI investments and initiatives.
Translate complex AI capabilities into business-focused insights and outcomes.
Lead cross-functional teams and ensure delivery accountability.
- AI Product Lifecycle Ownership
Own full lifecycle: Ideation Architecture Deployment Monitoring Optimization
Define and track KPIs and ROI for AI initiatives.
Oversee vendor/tool selection (LLM providers, MLOps platforms, data tools).
- Governance & Risk Management
Ensure compliance with data privacy, security, and ethical AI standards.
Identify risks and define mitigation strategies for enterprise AI adoption.
Establish AI governance frameworks and best practices.
Technical Skills Required
Architecture & AI Systems
LLMs, AI agents, RAG architectures, embedding pipelines
Machine learning & deep learning systems
Real-time + batch AI pipelines
Cloud & Infrastructure
Azure (preferred), AWS, or GCP
Kubernetes, Docker
Distributed systems & microservices design
Data & Engineering
Data pipelines (Spark, Kafka, Databricks, ADF)
Strong SQL + Python
MLOps
MLflow / Azure ML / Vertex AI
Model lifecycle management
Experience Required
12 15+ years in AI, data, or engineering roles
Experience in strategy consulting / enterprise transformation
Proven experience delivering production AI systems at scale
Exposure to CPG / manufacturing / supply chain domains (highly preferred)
Success Metrics
Business impact (ROI, cost savings, efficiency gains)
Successful production deployment of AI solutions
Adoption across business functions
Stakeholder satisfaction and strategic alignment