Director, AI Software Engineering

Diligent Corporation•Vancouver, BC

1d•CA$200,000 - CA$240,000•Hybrid

About The Position

We are seeking a hands-on Director of AI Software Engineering to lead and scale AI engineering efforts supporting multiple business units across Governance, Risk, and Compliance (GRC). This role sits at the intersection of product delivery, platform evolution, and applied AI—driving real-world impact across core workflows. This is not a pure management role. We are looking for a builder who leads from the front, someone who has recently written production code, shipped systems end-to-end, and can operate comfortably in ambiguity while aligning teams and stakeholders.

Requirements

10+ years in software engineering, with recent hands-on coding experience
Demonstrated track record of shipping production systems at scale
Experience with modern AI/ML systems (LLMs, NLP, search, or data platforms)
Strong backend engineering skills (e.g., Python, Java, or similar)
Experience with distributed systems, APIs, and cloud platforms (AWS/GCP/Azure)
Familiarity with modern AI stacks (e.g., vector search, embeddings, model serving, agent frameworks)
Experience leading engineering teams while remaining technically engaged
Ability to earn trust with senior engineers and challenge them constructively
Can simplify complex technical concepts for non-technical stakeholders
Comfortable operating at both executive and implementation levels
Bias for action; thrives in fast-moving, ambiguous environments
Focused on delivering measurable business impact—not just technical output

Nice To Haves

Experience in GRC, fintech, or regulated environments
Exposure to FedRAMP or similar compliance frameworks
Background in building AI-powered products (not just research)

Responsibilities

Lead AI Engineering Across GRC
Own delivery of AI-powered capabilities embedded directly into business unit workflows (e.g., risk analysis, compliance automation, reporting, due diligence)
Partner with product, data, and platform teams to translate business problems into scalable AI systems
Stay Hands-On
Contribute to architecture, code reviews, and critical path implementation
Prototype and validate new approaches (LLMs, agents, retrieval systems, classification pipelines, etc.)
Set engineering standards for performance, reliability, and cost efficiency
Build and Scale Teams
Lead and mentor a high-performing team of AI/ML and software engineers
Drive hiring, coaching, and career development
Establish a culture of ownership, speed, and technical excellence
Drive Execution
Deliver production-grade systems—not experiments
Balance speed with rigor (security, privacy, compliance)
Operate across multiple concurrent initiatives with clear prioritization
Communicate and Influence
Act as a bridge between engineering and business stakeholders
Clearly articulate trade-offs, risks, and outcomes to senior leadership
Align cross-functional teams around shared goals and timelines