[3 Days Left] Senior Manager / Associate Director -AI Systems Engineering

Role Overview

The Senior Manager / Associate Director of AI Systems Engineering will lead a 15-member team responsible for building and scaling the infrastructure that powers Pro-Vigil’s Visual Intelligence stack (VIS). This role combines hands-on technical expertise with organizational leadership to operationalize advanced AI models — from detectors and re-ID networks to activity classifiers, prediction models and genAI — across edge and cloud environments.

You’ll partner closely with the Edge / Cloud Engineering teams to ensure every model moves smoothly from lab to production with reliability, speed, and efficiency.

You Are Great At

Leading and mentoring a small but highly technical team of AI systems and platform engineers.

Designing robust AI inference architectures that integrate multiple vision models into a unified pipeline (edge + cloud).

Translating research prototypes into scalable production systems through efficient data, model, and API workflows.

Implementing model lifecycle processes — CI / CD for AI, versioning, canary deployment, monitoring, and rollback strategies.

Driving optimizations for latency, GPU utilization, and cost efficiency across heterogeneous edge and cloud infrastructure.

Collaborating across functions to align system capabilities with product roadmap milestones.

Instilling engineering discipline and accountability using metrics (MTTR, inference uptime, latency SLA, drift frequency, etc).

Building a strong feedback loop between field data, model updates, and production performance.

What It Takes

12–18 years of experience in software / AI engineering, with 5+ years leading small, multidisciplinary teams.

Bachelor’s or Master’s in Computer Science, Electrical Engineering, or related field.

Strong hands-on experience with :

• Python / C++ for systems integration.
• Containerization and cloud platforms (AWS or GCP preferred).
• Deep learning frameworks : PyTorch, TensorFlow, TensorRT or similar
• Proven success in deploying and optimizing deep-learning inference pipelines at scale
• Understanding of MLOps principles (data pipelines, drift detection, retraining automation, and so on)
• Experience managing cross-team dependencies and balancing research agility with production stability
• Experience with Agile software development frameworks
• Excellent communication and stakeholder management skills — able to work with leadership and customer base in the US

Nice to Have

Success in This Role Means

99% with measurable latency and cost improvements.