Staff ML Platform Engineer – MLOps

🕒 June 8

🌐 United States, Canada – Remote

🇺🇸 United States – Remote 🇨🇦 Canada – Remote

💵 $172k - $215k / year

⏰ Full Time

🔴 Lead

🏗️ Platform Engineer

👻 Ghost score 31%

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FutureFit AI

WebsiteLinkedInAll Job Openings

11 - 50 employees

🤖 Artificial Intelligence

🤝 B2B

☁️ SaaS

💰 Seed Round on 2022-03

Artificial Intelligence • B2B • SaaS

FutureFit AI is a software development company that empowers workers with an AI-powered tool designed to serve as a GPS for career navigation. By partnering with Fortune 500 companies, governments, and workforce development organizations, FutureFit AI provides solutions that support career transitions, from upskilling and reskilling to job placement. Utilizing over 350 million global talent profiles and proprietary algorithms, the company offers personalized roadmaps for learning and career support, effectively harnessing data to optimize workforce development and planning.

📋 Description

• Build and own the ML platform supporting ML and LLM-powered products • Evaluate current pipelines, data architecture, and ML workflows and create a prioritized improvement plan • Provision compute and manage reproducible training and serving environments • Maintain frameworks and packages across services and model images • Optimize latency, throughput, and infrastructure spend without destabilizing production • Build LLM infrastructure with cost-aware routing across models and providers • Implement prompt and response evaluation to validate routed-model quality • Establish model experimentation practices including shadow deployments, canaries, holdouts, and success criteria • Implement model and data monitoring, alerting, regression detection, lineage, and traceability • Operate and debug production ML systems, including models built by others • Own feature computation, storage, and consistent serving between training and inference • Establish deployment and monitoring standards for shared team ownership • Collaborate with the data and engineering team and mentor team members as needed

🎯 Requirements

• Staff-level, hands-on experience in MLOps, ML platform, or ML infrastructure • Experience standing up MLOps practices end to end, including model CI/CD, experiment tracking, model registries, deployment workflows, and monitoring • Production experience with LLM-based systems, including serving, prompt and response evaluation, routing across models and providers, and cost and latency management • Experience operating models in batch and real-time serving contexts • Hands-on experience with containers, compute provisioning, reproducible environments, and package/framework maintenance • Experience with A/B tests, shadow or canary deployments, holdouts, and pre-defined success criteria • Expertise in production ML observability and traceability, including drift/regression detection, alerting, lineage, and prediction reproduction • Hands-on operational experience debugging production ML incidents and fixing systems not originally built by the candidate • Comfort with data engineering, feature computation/storage, and training-serving feature consistency • Track record improving complex, fast-grown systems • Strong systems design ability and ability to build architecture directly • Ability to work in EST/EDT business hours • Legally eligible to work in Canada or the United States • Willingness to undergo a background check if required • Willingness to travel occasionally for offsites and team gatherings

🏖️ Benefits

• Remote-first work arrangement • Optional access to the Toronto office • Approximately 2-3 trips per year, including the company off-site • Reasonable accommodation for disabilities • Equal opportunity and inclusive workplace