AI Production Manager

About the RoleWe're looking for an AI Production Manager to lead the operational backbone that gets AI features from prototype to production and keeps them running reliably at scale. You'll sit a tthe intersection of ML engineering, product, and operations — owning the processes, pipelines, and people coordination that turn AI capabilities into dependable product features. This is a hands-on management role: you'll lead a small team of AI operations/production specialists while directly drivingcross-functional execution.WhatYou'll DoOwn the production pipeline for AI features — from model handoff through integration, testing, deployment, and monitoring in live products.Manage and mentor a team of AIproduction/operations specialists, setting priorities, reviewing work, and supporting their growth.Coordinate across ML, engineering, product, and design to ensure AI features ship on schedule, meet quality bars, and align with product requirements.Build and maintain release processes for AI models and features, including versioning, rollback plans, and staged rollouts.Monitor production AI systems —track performance, latency, cost, drift, and failure modes; drive remediation with engineering teams.Manage vendor and infrastructure relationships related to model hosting, compute, data labelling, or third-party AI tools.Own the production roadmap and timeline, communicating status, risks, and blockers to leadership and stakeholders.Drive quality and safety reviews before launch, working with responsible AI / trust & safety teams as needed.Continuously improve workflows— identify bottlenecks in the model-to-production pipeline and implement tooling or process fixes.Manage budget and resourcing for the production function, including compute costs and staffing needs.What We're Looking For4+ years of experience in technical program/production management, ideally with 1–2+ years managing a team.Experience working directly with AI/ML products — you understand model lifecycles, evaluation, deployment, and monitoring well enough to be a credible partner to ML engineers.Strong track record of shipping complex, cross-functional technical products on schedule.Comfortable in ambiguity — AI production processes are still being invented at most companies, and you'reexcited to build them rather than follow a playbook.Excellent communication skills; able to translate between technical ML concepts and business/productstakeholders.Familiarity with MLOps concepts and tools (e.g., model registries, CI/CD for ML, monitoring/observabilityplatforms) — hands-on technical depth is a plus but not required.Experience with project management and collaboration tools (Jira, Linear, Notion, Asana, or similar).People-management experience: hiring, coaching, performance reviews, and team development.Nice to HaveBackground in software engineering, data science, or ML engineering.Experience with LLM-basedproducts specifically (prompt pipelines, RAG systems, fine-tuning workflows, evaluation harnesses).Experience working in a regulated or safety-sensitive AI environment.Familiarity with responsible AI practices (bias evaluation, red-teaming, content safety review).