Purpose of the Job
We are looking for a Forward Deployed AI Engineer who can bridge the gap between business strategy and real-world AI delivery.

This role is about more than building models—it’s about taking AI from idea to production, integrating it into core systems, and ensuring it delivers measurable business value. You will combine hands-on engineering expertise with practical AI implementation, helping teams adopt AI in a way that is scalable, secure and usable.

What you’ll do
You will play a lead technical role in designing and delivering AI-enabled solutions across the enterprise.

Build and deliver AI solutions

Design, build, test, and deploy AI-enabled applications, services, and workflows

Work with LLMs, intelligent agents, and automation frameworks to solve real business problems

Take solutions from prototype to production, ensuring they are reliable and scalable

Own technical design

Lead architecture and design for LLM integrations

Retrieval-augmented generation (RAG)

Agent workflows and orchestration

API and enterprise system integrations

Ensure solutions are secure, reusable, and aligned with enterprise standards

Drive engineering standards

Define and apply reusable patterns and best practices for AI delivery

Improve how teams build, deploy, and scale AI solutions

Contribute to responsible and governed AI adoption

Support production and continuous improvement

Ensure solutions are production-ready (testing, monitoring, observability)

Troubleshoot issues, perform root cause analysis, and continuously improve systems





Optimize for performance, cost, reliability, and user experience

Partner across teams

Work closely with product, architecture, platform, security, and business stakeholders

Translate business needs into clear technical solutions and delivery plans

Influence decisions through technical expertise, not authority

What you bring
Engineering foundation

Strong experience building scalable, distributed systems

Deep knowledge of APIs, microservices, and service-based architectures

Cloud-native development (Azure preferred)

CI/CD, containerization, and deployment automation

Experience with event-driven systems, data pipelines and data platforms

AI / GenAI expertise

Hands-on experience building LLM-powered applications in production

Solid Experience with

Prompt design and evaluation

Model limitations (hallucination, variability, context constraints)

Agent design and orchestration workflows

Tool/API integrations

RAG and knowledge grounding patterns

Delivery and operational mindset

Experience across the full lifecycle: use case definition, solution design, integration, deployment, monitoring and optimization

Strong understanding of

AI observability (quality, latency, cost)

Reliability and system performance

Risk, security, and governance awareness

Experience working in regulated environments

Strong awareness of

Data privacy and security

AI governance and controls

Misuse prevention (incl. prompt injection risks)

Auditability and human-in-the-loop safeguards

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📌 Forward Deployed AI Engineer (Ontario)
🏢 Kinvie
📍 Ontario