Project Manager, Applied AI

🕒 May 12

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LILT AI

WebsiteLinkedInAll Job Openings

201 - 500 employees

Founded 2015

🤖 Artificial Intelligence

☁️ SaaS

🏢 Enterprise

💰 $25M Funding Round - LILT on 2025-06

Artificial Intelligence • SaaS • Enterprise

LILT AI is a multilingual AI platform that helps enterprises and public-sector organizations create, translate, verify, and manage content across languages at scale. It combines domain-specific AI models, continuous model training, and a human intelligence layer of professional linguists to deliver secure, brand-consistent localization, translation verification, and multilingual model development. The platform offers enterprise project management, 100+ native integrations, on-prem and air-gapped deployments, and tools to train, evaluate, and govern multilingual AI for use cases like website localization, product launches, technical documentation, and regulatory/compliance communications.

📋 Description

• Manage the full lifecycle of AI data projects, from scoping and guidelines creation to data delivery and post-mortem analysis. • Oversee large-scale data pipelines for multilingual data collection (audio, text, image) and LLM evaluation (RLHF, SFT, ranking, and safety testing). • Rigorously monitor and report on key performance indicators, including throughput, quality, and productivity. • Maintain dashboards to visualize project health and flag bottlenecks in real-time. • Manage relationships with data experts and crowd pools, ensuring adherence to SLAs regarding localized nuances and linguistic accuracy.

🎯 Requirements

• 3-5+ years of project management experience, specifically within AI/ML data operations. • Strong understanding of LLM training processes (Pre-training, SFT, RLHF) and evaluation methodologies (Human-in-the-loop, red teaming). • Advanced proficiency in Excel/Google Sheets; ability to write SQL queries to extract and analyze performance data. • Proven track record using Agile, Scrum, or Kanban methodologies to manage complex workflows. • Exceptional ability to write clear, unambiguous guidelines for multilingual audiences. • Fluency in a second language is highly desirable. • Experience with data annotation platforms (e.g. Scale AI, Super Annotate) and project management tools (e.g. Jira). • Background in ML Engineering, Computer Science, Data Science and Project Management training.