We are looking for a Staff-level Forward Deployed AI Engineer to design, build, and deliver AI-powered applications that create measurable business impact.

This is a hands-on engineering role with strong design responsibility — you will spend most of your time writing code, integrating systems, and taking solutions to production, while also shaping practical, scalable designs that ensure what you build can operate reliably at enterprise scale.

You will work closely with business stakeholders to identify high-value opportunities, rapidly prototype solutions, and evolve them into well-architected, production-grade systems.

What You Will Be Responsible For: You will play a lead technical role in designing and delivering AI-enabled solutions across the enterprise.

1. Build & Ship AI Applications (Primary Focus)

Design, develop, and deploy

AI-powered applications and workflows

Write production-quality code across:

Backend services and APIs AI orchestration layers and agents Enterprise integrations

Rapidly prototype solutions and

iterate them into scalable production systems

Own delivery

end-to-end: build, test, deploy, monitor, and improve

2. Design Practical, Scalable AI Systems

Translate use cases into

clear, implementable system designs

Make architecture decisions that balance:

Speed of delivery Scalability and reliability Cost and operational efficiency

Define patterns for:

API-first integrations AI orchestration and workflows Reusable services and components

Ensure systems are

straightforward enough to build quickly

, but





structured enough to scale

3. Integrate AI into Real Enterprise Workflows

Embed LLM capabilities into

products, internal tools, and business processes

Build and maintain

APIs and system integrations

Implement

agent workflows and orchestration logic

that solve real operational problems

Optimize systems for

performance, resilience, and cost efficiency

4. Partner with Business & Deliver Outcomes

Work directly with stakeholders to

understand problems and validate solutions

Translate requirements into

working software quickly (days/weeks, not months)

Iterate based on feedback and usage to drive

measurable impact

5. Contribute to Engineering Standards & Reuse

Build and contribute to

shared libraries, templates, and services

Establish

practical patterns based on real implementations

Help evolve internal platforms through

code and working solutions

, not just design artifacts

6. Build Within a Governed AI Environment

Implement

secure and reliable AI solutions in practice

, including:

Prompt safety and validation Injection/misuse prevention Observability and traceability

Align implementations with

enterprise security,



privacy, and compliance requirements

Technology Environment

Cloud & Platform:

Microsoft ecosystem (Azure)

AI Models:

Claude and other enterprise-approved LLMs

Architecture Style:

API-first, event-driven, and modular services

Core Focus:

AI application engineering Orchestration and agent workflows Enterprise integrations

What you bring: Hands-On Engineering Strength (Critical)

Proven ability to

build and ship production systems at scale

Strong experience in:

Backend development and API design Cloud-native systems (Azure preferred) Integration-heavy, distributed applications

Comfortable operating in a

high-output, hands-on environment

System Design & Architecture Judgment

Ability to design

clean, practical architectures

that support real-world constraints

Experience making trade-offs across:

delivery speed vs scalability simplicity vs flexibility

Can move fluidly between

coding and design thinking

AI / GenAI Development

Hands-on experience building

LLM-powered applications in production

Strong understanding of:

Prompt design and evaluation Agent-based workflows and orchestration Integrating AI into production systems

Ability to

debug, tune, and improve AI behavior in code

Execution Mindset

Bias toward

shipping and learning from production usage

Comfortable moving from

idea → prototype → production

Strong ownership:

you build it, you run it

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📌 Forward Deployed AI Engineer (Toronto)
🏢 EQ Bank | Canada's Challenger Bank
📍 Toronto