Lead AI Platform Engineer

As the backbone of Prolific's AI production lifecycle, this role bridges research and practical implementation. The engineer ensures teams have infrastructure, pipelines, and deployment strategies for shipping AI models and agents at scale.

Primary Responsibilities

Infrastructure & Platform Engineering

• Design and maintain scalable cloud environments (GCP/AWS) using Terraform
• Manage GPU/TPU resource allocation
• Build internal services and CLI tools

ML & LLM Orchestration

• Design CI/CD and training pipelines (GitHub Actions, MLFlow, Vertex AI)
• Develop model serving patterns and Kubernetes deployments
• Manage vector databases and RAG systems
• Implement monitoring for model drift and resource utilization

Performance & Optimization

• Reduce inference latency through quantization and distillation
• Address scaling bottlenecks in serverless/containerized deployments
• Optimize GPU utilization and cloud costs
• Solve cold-start issues

AI Enablement

• Define and create tooling and service templates around agent deployment
• Develop no-code/low-code workflows (n8n, LangSmith)
• Support platform adoption across teams

Key Requirements

• 5+ years with cloud infrastructure and infrastructure as code
• ML/LLM lifecycle experience (training, hosting, optimization, observability)
• Collaboration experience with researchers and data scientists
• Understanding of ML fundamentals and modern generative AI technologies

Location: Remote, UK

Compensation: Competitive salary, benefits, and remote working.