Senior Advisor, AI Engineering Frameworks & Tooling

Questrade Financial Group

1d•Remote

About The Position

We're looking for a hands-on technologist and evangelist who can reimagine how an entire bank operates — not just Engineering, but Legal, Compliance, Accounting, Settlements, and Operations. This is not a management role. It's an Individual Contributor / Senior Advisor position for someone who builds AI frameworks, tooling, and repeatable processes that fundamentally transform how departments do their work. The change we need is profound. Today, a classic bank legal team manually reviews contracts against B-10 and B-13 regulations. Settlements teams reconcile transactions through spreadsheets and manual checkpoints. Accounting spends weeks closing month-end with human verification at every step. Compliance audits are reactive — findings surface after the fact, not in real time. We want someone who looks at each of these, works alongside those teams, and designs AI-powered frameworks that don't just assist — they reinvent the workflow entirely. The role is to create the patterns, build the first working version, and enable the department to own and evolve it from there. Automated contract review that flags regulatory gaps before a human ever reads the document. Month-end close processes that run continuously instead of in a quarterly crunch. Audit frameworks that proactively surface findings with evidence, not audit trails someone has to dig through after the fact. The ideal candidate has shipped real AI-augmented workflows — not just prototypes. They've built the kind of tooling that turns a month of specification work into an afternoon, that makes a compliance review happen in minutes instead of days, and that makes entire departments rethink what's possible. They take feedback as fuel, evangelize by demonstrating results, and constantly reinvent their own methods before asking others to change theirs.

Requirements

Deep hands-on experience building AI-assisted workflows — not just using AI tools, but creating frameworks others adopt. Bonus if you've done this for non-technical teams, not just developers.
Strong software engineering foundation: you can design APIs, write OpenAPI specs, build Docker environments, script CI/CD pipelines, and operate in complex distributed systems. You build the plumbing that makes AI tooling production-grade.
Demonstrated ability to learn and internalize business domains quickly — you can sit with a compliance officer, understand their regulatory review process, and come back with a working AI framework that transforms it. Financial services experience is a plus, but the ability to go deep in any domain is what matters.
Natural evangelist — you get energy from showing people a better way, and you handle pushback and retroaction with curiosity rather than defensiveness. You understand that reinventing how someone works requires trust, not just technology.
Experience with prompt engineering, AI agent orchestration, and/or MCP (Model Context Protocol) server design.
Comfortable operating across the full stack: infrastructure (Docker, GCP/Azure), backend (C#, Python, Node), API design (OpenAPI, AsyncAPI), and developer experience tooling.
Self-directed and constantly reinventing — you don't wait for a mandate to experiment, and your side projects often become the team's standard tooling.
Degree, diploma, or equivalent directly related work experience.
Startup or scale-up experience — comfortable with ambiguity, fast iteration, and building from scratch rather than following established procedures. The dynamic nature of this mandate rewards people who've operated where process doesn't exist yet and had to create it.

Nice To Haves

Contributions to open-source AI tooling or developer experience projects.
Experience building AI frameworks that span non-technical departments (Legal, Compliance, Operations).
AI Tooling: Claude Code, MCP servers, custom slash commands/skills, prompt pipelines, Gemini, ChatGPT, GLM, Qwen, LangGraph, etc.
Runtime: REST APIs (C# microservices), SPAs (Angular), OpenAPI/AsyncAPI specifications
Data: Google Cloud SQL, MongoDB, Redis, BigQuery, SQL Server
Streaming: Google Pub/Sub, Kafka
Infrastructure: GCP, Infra as Code (terraform, helm, etc.), Docker, containerized dev environments
Tools: GitLab, Jira, Confluence, automated CI/CD

Responsibilities

Reinvent non-technical department workflows with AI — This is the primary mandate. Embed yourself in Engineering, Legal, Compliance, Accounting, Settlements, and Operations to understand their actual work — then build AI frameworks that transform it. Examples of the depth we expect: Legal: Build contract review frameworks that automatically assess documents against regulatory requirements (B-10, B-13, consumer protection), flag gaps, and produce structured findings — not just summaries, but actionable compliance assessments. Compliance & Audit: Create AI-driven audit frameworks that continuously monitor processes and proactively surface findings with supporting evidence. Move from reactive "audit after the fact" to real-time automated audits that catch issues as they happen. Settlements: Redesign reconciliation workflows so that exception handling, break detection, and resolution are AI-assisted end-to-end — not spreadsheet-driven. Accounting: Tackle the month-end close. Build tooling that automates verification steps, flags anomalies early, and compresses a multi-week process into days.
Build AI frameworks and developer tooling — Design and implement reusable AI-assisted workflows (slash commands, prompt engineering pipelines, Claude Code skills, MCP integrations) that accelerate software delivery, API design, specification authoring, story writing, and compliance checks across Engineering teams.
Create AI-native development environments — Architect containerized, composable development platforms where AI agents operate the full toolchain (build, test, lint, deploy, simulate) so developers describe intent in plain language instead of managing infrastructure.
Be the evangelist — Demonstrate results, not slide decks. Walk into a department, understand their pain, build a working prototype, and show them what their work looks like with AI. Run workshops, pair with teams, publish internal guides, and build adoption through undeniable productivity gains. Take retroaction positively and iterate publicly.
Stay best-in-class — Continuously research emerging AI capabilities (new models, agent frameworks, MCP servers, code generation patterns), experiment hands-on, and translate findings into production-grade tooling that others can adopt immediately.
Bridge business and technology — Go deep into the business domains. You can't reinvent a settlement process you don't understand. Learn the regulations, the accounting rules, the operational constraints — then build AI tooling that respects that complexity rather than oversimplifying it.
Champion continuous delivery for AI tooling — Version-control prompt engineering, skill definitions, and framework configurations. Treat AI workflows as software: tested, reviewed, documented, and shipped through CI/CD.
Mentor and multiply — While not a people manager, actively grow AI literacy across engineering and non-engineering teams. Turn individual expertise into organizational capability. The goal is that every department eventually has AI-native workflows they own and evolve.