ML Ops/Production Engineer

Ent

1d•Hybrid

About The Position

About Ent Ent is the intent-aware workspace security platform for securing human and AI-driven work. Built to protect productivity, the new attack surface, Ent understands not just what users and agents do but why, and intervenes at the moment of risk before incidents occur. Where existing tools see events, Ent sees intent, so security teams can step in at the moment of risk instead of investigating days later. Founded by Lou Manousos and Brandon Dixon, co-founders of RiskIQ (acquired by Microsoft) and the team behind Microsoft Security Copilot, Ent is in production with Global 2000 customers across hospitality, financial services, and defense, and backed by Decibel, Sequoia, Crosspoint Capital, Craft Ventures, Shield Capital, Felicis, and In-Q-Tel. We’re now hiring the team that will define this category. How We Work Customer first. The product and the business are built around problems we’ve watched real security teams struggle with — not the other way around. Every roadmap conversation starts with what a CISO told us last week. Humble. No drama. We hire people who share the mission and trust each other to deliver. Teamwork over showmanship. Accountability over politics. The work speaks louder than the person doing it. Urgency. The window to build a durable security company in the AI era is open right now and it will not stay open. The shot clock has started. We move at the speed of the people we want to protect. About the Role We are seeking an engineer who is passionate about scientific computing, data processing AI systems. This role is focused on solving hard problems in the area of optimization of deployment of heterogeneous set of models (small, medium, LLM), ensuring robustness, long-term maintainability. Innovating around frameworks that enable fast experimentation with large datasets, training/validating models and CI/CDs. This role will span development of production systems and as well as supporting systems such as to enable training/validation of new models, management of data etc.

Requirements

Experience with building production grade distributed systems involved in processing large datasets, ML/AI, LLMs.
Comfortable with designing/building systems with ambiguous requirements, anticipating changes or expansion.
Delivered results (software) in academic/industry settings

Responsibilities

Optimization of deployment of heterogeneous set of models (small, medium, LLM)
Ensuring robustness, long-term maintainability
Innovating around frameworks that enable fast experimentation with large datasets, training/validating models and CI/CDs
Development of production systems
Development of supporting systems such as to enable training/validation of new models, management of data etc.