Staff Engineer (AI & Engineering)

EQ Bank · Toronto

OtherAI COEHybridFull Timeposted 4w ago

We are looking for a Staff Engineer, AI & Engineering who can bridge deep software engineering expertise with practical AI implementation. This role is ideal for a senior technical leader who has built scalable software systems and has experience leveraging AI technologies to solve complex business problems.

You will partner closely with Engineering, Product, Data, and Technology leaders to design and deliver modern, resilient, and intelligent solutions. While experience with AI and machine learning technologies is important, this role is fundamentally an engineering leadership position focused on architecture, platform development, system design, and software delivery excellence.

This is a hands-on role requiring strong technical depth, architectural thinking and the ability to influence engineering direction across multiple teams.

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)

8+ years of software engineering experience building and delivering scalable, production-grade applications and platforms.Demonstrated success leading complex technical initiatives from design through deployment and ongoing operations.Strong engineering fundamentals with the ability to influence technical direction across teams and organizations.

System Design & Architecture Judgment

Deep expertise in designing scalable, resilient, and maintainable software architectures.
Experience making trade-offs across:

delivery speed vs scalability
simplicity vs flexibility
Can move fluidly between coding and design thinking

AI / GenAI Development

3+ years of hands-on experience building and deploying AI/Generative AI solutions in production environments.
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