AI Software Development Manager

JSI•Ottawa, ON

1d•Hybrid

About The Position

When data is complex and the mission is critical, law enforcement, intelligence, and security organizations around the world trust JSI. Our 4Sight platform fuses disparate data sources — communications, location, OSINT, financial activity, and more — to expand awareness, surface patterns, and simplify data complexity for the teams that rely on it. AI and machine learning sit at the center of how 4Sight does that work, and the quality of those capabilities has direct, real-world consequences for the investigations and operations they support. We are hiring an experienced Engineering Manager to lead two agile AI/ML teams (~10 engineers total) building production AI capabilities inside our 4Sight platform. You will be accountable for delivery, engineering quality, and team performance, and you will help shape the technical direction in partnership with senior engineering leadership and architects. This is a hands-on technical leadership role. We want a manager who still reads code, engages in design discussions, and exercises real judgment on architecture and trade-offs alongside their teams. You will operate in a fast-moving AI environment where priorities shift, ambiguity is the norm, and the bar for production reliability is high. Before you apply: this role requires eligibility for a Government of Canada Top Secret security clearance. The position is based in Kanata, Ontario, with at least two days per week of in-office collaboration.

Requirements

10+ years of software engineering experience, including 5+ years managing engineering teams with direct delivery accountability
Track record of leading teams through ambiguous, evolving roadmaps where the right answer wasn't obvious at the start
Experience owning production systems end-to-end: release quality, on-call response, and post-incident learning
Strong design and architecture judgment — able to engage credibly with senior engineers on technical trade-offs and push back when warranted

Nice To Haves

Direct experience managing AI/ML or data engineering teams, not just teams adjacent to them
Shipped production ML systems and lived with the operational reality — drift, retraining, evaluation, incident response on model behavior
Worked with large-scale data infrastructure: streaming pipelines, distributed processing, feature stores, or comparable
Background in one or more of: entity resolution, graph/link analysis, NLP, anomaly detection, or geospatial analytics
Experience in regulated, mission-critical, or national-security adjacent environments
Prior Government of Canada or Five Eyes clearance held in the last 24 months

Responsibilities

Lead, manage, and mentor two agile teams of software developers, data scientists, and AI engineers (~10 direct reports)
Hire, develop, and retain high-performing engineering talent
Conduct performance management, career development, and coaching
Foster a culture of accountability, collaboration, and continuous improvement
Set clear expectations and ensure alignment with team and organizational goals
Act as an escalation point for team-level challenges and cross-team dependencies
Accountable for the delivery outcomes, engineering quality, and performance of both teams
Ensure predictable, sustainable delivery practices aligned with business objectives
Manage scope, risks, and dependencies across concurrent initiatives
Partner with Product Management to translate business goals into executable plans
Ensure high-quality releases and effective handling of production issues and support needs
Evolve agile practices, testing discipline, and release processes to improve delivery outcomes and team effectiveness over time
Set and execute the technical direction for your product area
Maintain sufficient technical depth to guide design decisions and review critical architecture
Partner with senior engineers to ensure sound technical execution and maintainability
Identify and manage technical debt, balancing short-term delivery with long-term system health
Lead delivery of AI/ML capabilities across investigative use cases — entity resolution, link analysis, anomaly and pattern detection, NLP on unstructured communications, and similar problems against large, heterogeneous datasets
Drive the path from experimentation to production: dataset curation, model evaluation against operational metrics, deployment, monitoring for drift, and retraining cycles
Hold a high bar for model validation in a domain where false positives and false negatives both have real consequences for investigations and the people involved
Partner with data and platform teams on the infrastructure that makes production ML reliable — feature stores, evaluation harnesses, observability, and lifecycle tooling
Champion responsible-AI practices: bias review, explainability where it matters, auditability, and clear human-in-the-loop boundaries