Manager - Applied AI

Role Overview

QuillBot is looking for an Applied AI Manager to own the end-to-end productionization of AI research into reliable, scalable, and continuously improving production systems.

As the Manager of AI & Data Engineering, your primary responsibility is to productionize all AI models developed by the AI team. This includes building and operating the data pipelines, training and inference infrastructure, deployment workflows, monitoring systems, and model retraining that allow models to move predictably from research to production, and stay healthy once they’re there.

You will lead a hybrid team of MLOps engineers and Applied AI scientists and act as the primary bridge between AI Research and production. Once a model is developed, your team owns it — including performance, reliability, cost efficiency, and long-term evolution.

This is a mission critical hands-on leadership role that is central to the success of QuillBot, combining deep technical execution with team leadership and cross-functional ownership.

Responsibilities

• Productionize AI models developed by the Research team, owning the path from research validation to stable, scalable production deployment.
• Build and operate data pipelines, training and evaluation infrastructure required to support continuous model iteration.
• Own the full lifecycle of production AI models, including:
• Design and maintain low-latency, cost-efficient inference systems with clear SLAs.
• Build and scale AI infrastructure that supports both large-scale training and reliable production inference.
• Partner closely with AI Research to define model readiness criteria, evaluation standards, and handoff processes.
• Maintain a controlled backlog of research models awaiting productionization and manage prioritization across stakeholders.
• Lead and mentor a high performance, hybrid team of MLOps engineers and Applied AI scientists.
• Set execution standards focused on delivery velocity, reliability, and iterative quality improvements.
• Build and maintain CI/CD systems for ML, including experiment tracking, model versioning, observability, and alerting.
• Contribute to architectural decisions, AI platform roadmap planning, and documentation.
• Collaborate with cloud and AI providers (e.g., AWS, GCP, OpenAI) to integrate tooling, optimize costs, and unlock platform capabilities.
• Champion MLOps, data engineering, and applied AI best practices across the organization.

Qualifications

• 6+ years of experience in MLOps, ML Engineering, Applied AI, or Data Engineering, with significant ownership of production ML systems.
• Proven track record of taking models from research or experimentation into production and operating them at scale.
• Strong understanding of ML/DL fundamentals and the full model lifecycle.
• Experience leading or mentoring senior engineers and/or scientists.
• Strong background in building data pipelines and model monitoring systems.
• Experience with performance optimization techniques such as quantization, distillation, TensorRT-LLM, FasterTransformer, or similar.
• Strong DevOps instincts applied to ML systems.
• Proven ability to lead complex technical projects end-to-end with minimal oversight.
• Ability to balance exploratory upstream work with downstream production commitments.
• Strong collaboration and communication skills, able to work cross-functionally and drive technical clarity.
• Ownership mindset, comfortable making decisions and guiding others in ambiguous problem spaces.

Benefits & Perks

• Competitive salary and annual bonus
• Medical coverage
• Life and accidental insurance
• Vacation & leaves of absence (menstrual, flexible, special, and more!)
• Developmental opportunities through education & developmental reimbursements & professional workshops
• Maternity & parental leave
• Hybrid & remote model with flexible working hours
• On-site & remote company events throughout the year
• Tech & WFH stipends & new hire allowances
• Employee referral program
• Premium access to QuillBot