Lead AI Platform Engineer at Prolific

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.