Remote

Head of AI

Engineeringfull timelead level$130k-$180k
AI ScreenedRemote B2BEU Talent Pool1 applicants
For hiring agencies & HR teams

EU engineers, ready to place with your US clients

Pre-screened on AI. Remote B2B contracts. View 5 full profiles free — AI score, skills report, interview questions included.

About This Role

Lead AI strategy and development at an AI-first recruiting platform transforming talent acquisition through machine learning. As Head of AI, you'll architect and oversee our core ML/LLM systems that power candidate matching, skill extraction, and intelligent screening—directly impacting product performance and customer outcomes. This is a hands-on leadership role where you'll drive technical vision, build and mentor a growing engineering team, and collaborate cross-functionally with product and go-to-market teams in a fast-moving B2B SaaS environment. You'll own the full AI/ML stack from model development and training pipelines to production deployment and optimization. Working with a EU-based talent pool in a remote-first culture, you'll balance strategic direction with technical execution, making critical decisions on model selection, data infrastructure, and emerging AI capabilities (LLMs, RAG, embeddings). Your leadership will shape how we leverage AI to deliver competitive differentiation in the recruiting space.

Requirements

  • Lead AI/ML teams of 3+ engineers with proven experience scaling models from prototype to production
  • Demonstrate 8+ years in machine learning engineering, AI product development, or data science roles
  • Design and implement end-to-end ML systems including data pipelines, model training, and inference optimization
  • Develop hands-on expertise with modern LLM frameworks, embedding models, and RAG architectures
  • Define AI strategy aligned with product roadmap and deliver measurable ML metrics and model performance improvements
  • Architect scalable ML infrastructure on cloud platforms (AWS/GCP/Azure) with strong MLOps practices

Required Skills

Machine Learning EngineeringLarge Language Models (LLMs)PythonML Ops / MLOpsVector Databases & EmbeddingsRetrieval-Augmented Generation (RAG)Data InfrastructureDeep LearningNLPTeam Leadership