AI Engineer (Advanced GenAI & Production AI Focus)

Position: AI engineer/Architect
Location: Toronto Canada(On-site / Hybrid)
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

1. 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.

2. AI Solution Architecture

Architect end-to-end AI systems including:

o Data pipelines

o Feature/embedding pipelines

o LLM integrations and AI agents

o 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.

3. 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.

4. 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.

5. 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).

6. 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