Forward Deployed AI Engineer

Kinvie; Toronto; On-site; CAD 90,000 - 120,000; Full time

Job summary

Kinvie is seeking a Forward Deployed AI Engineer to bridge business strategy and AI delivery. The role involves designing AI-enabled solutions, ensuring reliability and scalability through collaboration with various teams.

The ideal candidate has strong experience in building scalable systems, hands-on LLM application experience, and familiarity with regulated environments. You will influence technical solutions and drive responsible AI adoption throughout the organization.

Qualifications

• Strong experience building scalable, distributed systems.
• Hands-on experience building LLM-powered applications in production.
• Experience working in regulated environments.

Responsibilities

• Design, build, test, and deploy AI-enabled applications.
• Lead architecture for LLM integrations and ensure security.
• Ensure solutions are production-ready and troubleshoot issues.
• Work closely with product, architecture, platform, and security teams.

Skills

Building scalable, distributed systems

APIs, microservices, and service-based architectures

Cloud-native development (Azure preferred)

CI/CD, containerization, and deployment automation

Event-driven systems, data pipelines and data platforms

Prompt design and evaluation

Model limitations (hallucination, variability, context constraints)

Agent design and orchestration workflows

Tool/API integrations

RAG and knowledge grounding patterns

Job description

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

Strong 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