VP Of Engineering

At Trackforce, we are transforming how physical security operations are managed around the world. As the leading SaaS platform for physical security workforce management, we provide security companies and organizations with a streamlined solution to manage their guard forces, respond faster, operate more efficiently, and reduce costs — all while staying focused on safety and protection. We support 4,600+ clients across more than 50 countries and are proud to be a growing team of 300+ professionals. With our headquarters in Dallas, Texas, and Centers of Excellence in Montreal, Quebec and Wroclaw, Poland, we collaborate across regions and time zones in a hybrid work environment that combines flexibility, connection, and meaningful impact. Role Summary TrackTik is scaling its end-to-end workforce management platform used by security service organizations worldwide. We’re now looking for a VP of Engineering who can drive predictable delivery, modernize the architecture, strengthen engineering culture, and now—critically—lead our evolution into an AI-augmented product and AI-driven engineering organization. You will partner tightly with the CTO to translate strategy into an engineering operating model that ships, scales, and stays reliable. Expect to lead through ambiguity, remove blockers with urgency, and turn a historically reactive environment into a high-performance, outcome-driven engineering organization. Key Responsibilities

Technology & Architecture Leadership

Define and enforce the technical vision and architectural roadmap across multiple product functions (mobile, scheduling, time/attendance, reporting, integrations).

Modernize legacy components while ensuring continuity and stability for enterprise customers.

Champion secure-by-design and performant systems; drive a shift to observability-first engineering.

Establish a technical foundation for AI services, vector stores, data pipelines, and scalable inference infrastructure.

Implement guardrails for responsible AI use: data privacy, model validation, auditability, and customer-facing transparency.

Delivery & Execution

Own predictable delivery—quarterly planning, execution, quality, and release cadence.

Build and scale engineering processes that balance speed with stability (LEAN, CI/CD, trunk-based development, etc.).

Reduce technical debt with a clear, measurable, investment plan.

Drive end-to-end delivery for AI features—from experimentation to product ionization—with clear KPIs and success metrics.

Introduce pipelines for model testing, deployment, monitoring, and lifecycle management.

Team Leadership & Organizational Design

Lead and grow a multi-disciplinary engineering organization (backend, frontend, mobile, QA).

Develop high-performing engineering leaders; set clear expectations and enforce accountability.

Strengthen engineering culture based on ownership, transparency, and continuous improvement.

Build internal AI capabilities by hiring AI engineers, integrating AI specialists into squads, and upskilling existing engineers through training and enablement.

Partner with Product to mature AI literacy across the entire organization.

Cross-Functional Collaboration

Partner with Product Management on roadmap definition, scoping, estimates, and tradeoff decisions.

Collaborate with Customer Success, Support, and Sales Engineering to close feedback loops and accelerate quality improvements.

Represent engineering at the senior leadership level and to customers when needed.

Collaborate with Product to identify where AI enhances workflows: incident prediction, scheduling optimization, reporting automation, guard activity intelligence, anomaly detection, and operational forecasting.

Work directly with customers on AI adoption, ensuring enterprise readiness and alignment with compliance requirements.

Operational Excellence

Own uptime, performance, incident response, security posture, and SLO/SLI maturity.

Drive metrics-driven engineering (DORA, defect rates, cycle time, operational KPIs).

Build out robust on-call, escalation, and root-cause analysis processes.

Use AI to optimize internal operations—e.g., automated triage, anomaly detection in logs, predictive scaling, and support-ticket classification.

Ensure AI features meet enterprise reliability and audit expectations, including drift detection and model performance monitoring.

Strategic Impact

Translate company strategy into engineering priorities and resourcing plans.

Forecast hiring, budgets, vendor needs, and long-term technical investments.

Act as a key voice in corporate decision-making as the business scales into new markets and product capabilities.

Required and Preferred Experience

Required Experience

10+ years in software engineering with 5+ in senior engineering leadership roles (Director/VP).

Proven success leading engineering teams in a SaaS company with large enterprise clients.

Demonstrated ability transforming engineering orgs and modernizing legacy systems without destabilizing critical operations.

Strong track record managing organizations of 30–80+ engineers across multiple squads.

Experience with cloud-native systems (AWS preferred), distributed architecture, and security-sensitive environments.

Excellent communicator who can handle exec-level debates, customer escalations, and difficult prioritization discussions.

Experience shipping AI-powered features.

Familiarity with modern AI tooling: LLMs, vector databases, embeddings, ML Ops, and cloud-based AI infrastructures.

Preferred Experience

Background in workforce management, physical security tech, compliance-heavy SaaS, or operationally intensive B2B & B2C products.

Experience integrating with large enterprise ecosystems (HRIS, payroll, scheduling, IoT/guard-tour systems).

Prior success supporting a platform undergoing international scale or multi-product expansion.

Experience using AI to optimize workforce operations, predictive analytics, or scheduling automation.

Experience evaluating LLM vendors, fine-tuned models, and domain-specific AI approaches.

Success Looks Like

Predictable, high-quality releases—no surprises, no thrash.

Engineering leaders who own outcomes and deliver autonomously.

Architecture that scales and doesn’t slow down feature velocity.

A culture where technical decisions are intentional, measurable, and aligned with the business.

Material reduction in outages, defects, and firefighting.

A roadmap that engineering can deliver without heroic effort.