Forward Deployed AI Engineer

EQ Bank | Equitable Bank equitablebank.ca Open · verified Sep 24, 2026

Hybrid Toronto Full-time Mid-level Solutions & Sales Engineering

At a glance

Mid-level Solutions & Sales Engineering role at EQ Bank | Equitable Bank. Toronto · full-time.

Pay not stated

RoleSolutions & Sales Engineering

SeniorityMid-level

LocationToronto

WorkplaceHybrid

EmploymentFull-time

PostedJun 22, 2026 · 3mo ago

Role brief

Growth Roles summary, based on the employer's posting.

What you'll do

Build, test, launch, monitor, and improve AI applications, backend services, APIs, and agents
Turn promising prototypes into scalable systems with practical architecture and production-quality code
Connect LLM capabilities to products, internal tools, business processes, and enterprise systems
Work with stakeholders to turn operational problems into working software and measurable outcomes
Create reusable libraries, templates, services, and governed patterns for secure AI delivery

What you bring

A record of shipping production systems at scale through backend engineering and API design
Experience with Azure or comparable cloud-native, distributed, integration-heavy applications
Hands-on production work with LLM applications, prompt evaluation, and agent orchestration
Sound judgment when balancing delivery speed, scalability, simplicity, flexibility, and operating cost
Ownership from initial idea through prototype, production operation, debugging, and improvement

Who this fits

This role suits a hands-on engineer who can switch between coding, architecture, stakeholder discovery, and production support. You should be comfortable delivering quickly, learning from real usage, and operating AI systems within security, privacy, compliance, safety, and observability requirements. The position is hybrid in Toronto and is described as Staff-level despite the mid-seniority label.

From the employer

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 simple 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