AI Platform Engineer (Web3)

TL;DR

AI Platform Engineer (AI/Web3): Building foundational infrastructure for AI development, including model serving, evaluation harnesses, and observability layers with an accent on scalability and reliability. Focus on orchestrating agentic workflows and creating tooling that enables domain teams to integrate AI safely and effectively into hirify.global's crypto-compliance products.

Location: Must be based in or able to work from London, UK (Hybrid role).

Company

hirify.global is a leader in crypto-asset risk management, providing compliance and investigative solutions to make the digital asset ecosystem safer.

What you will do

• Build and maintain core AI platform components like model serving infrastructure and prompt pipelines.
• Support the development of agentic workflows, including orchestration and reliability mechanisms.
• Instrument AI systems for observability, tracking latency, token costs, and quality signals.
• Develop frameworks for prompt versioning, testing, and governance to ensure baseline quality.
• Collaborate with domain teams to integrate AI platform foundations into their specific workflows.
• Monitor the evolving AI landscape to assess and implement new model capabilities and orchestration patterns.

Requirements

• Hands-on experience building with LLMs or ML systems in production, side projects, or academic settings.
• Solid software engineering fundamentals with a focus on clean, testable, and maintainable code.
• Familiarity with AI protocols like MCP, A2A, or ACP.
• Understanding of context window management and agentic workflow optimization.
• Experience with at least one of: API orchestration, data pipelines, model evaluation, or observability tooling.
• Must be based in the UK to support the hybrid working model.

Nice to have

• Experience with LLM frameworks like LangChain, LangSmith, or Databricks AgentBricks.
• Background in prompt engineering or evaluation dataset design.
• Interest in the crypto and digital assets ecosystem.
• Experience in regulated or compliance-adjacent domains.
• Familiarity with ML lifecycle tooling like MLflow or Databricks ML.

Culture & Benefits

• Hybrid working model with a £500 home office budget.
• Option to work from almost anywhere for up to 90 days per year.
• $1,000 annual Learning & Development budget.
• 25 days of annual leave plus bank holidays and an extra day for your birthday.
• Enhanced parental leave (16 weeks fully-paid).
• Private health insurance, mental health support, and life assurance.