Engineering Manager - DevOps & AI Platform

In this role, you lead two connected functions—DevOps and AI Platform—ensuring a latency‑sensitive screening platform is reliable, scalable, and cost‑effective. You shape the operating model, grow the team, and drive best practices across reliability and AI agent frameworks. You bridge the center of excellence with product teams to accelerate safe, AI‑driven software delivery. The role sits at the core of Elliptic’s mission to keep crypto markets safe at scale.

• Hybrid working
• Remote work budget up to 500
• Learning & Development budget 1000
• 25 days annual leave + bank holidays
• Birthday day off
• Enhanced parental leave (16 weeks, fully paid)

• Lead and develop ~12 engineers across DevOps and AI Platform, including hiring, leveling, coaching, and career growth
• Set strategy and operating model for DevOps focusing on release safety, developer experience, observability, cost, and latency‑sensitive scaling
• Lead the AI Platform as a centre of excellence for agent frameworks, MCP management, evaluation, and shared AI infrastructure
• Disseminate best practices from the centre of excellence to product teams and incorporate feedback to refine approaches
• Partner with the AI Platform technical lead to shape technical direction with a focus on people leadership and delivery
• Own cost, scalability, and efficiency of infrastructure with a FinOps mindset
• Champion SRE and reliability practices across the business, coordinating with the SRE function
• Support infrastructure and process maturity needed for ISO 27001 and SOC2 certifications, in collaboration with InfoSec

• Track record of managing and growing DevOps, platform, or infrastructure engineers
• Strong infrastructure background (cloud, IaC, CI/CD, observability) with strategic decision‑making experience
• Curiosity about AI with hands‑on exposure to AI, agents, and evaluation frameworks
• Understanding of AI‑assisted SDLC and guardrails to maximize leverage
• Kubernetes literacy with hands‑on engineering experience
• Practical AI fluency and ability to set norms for responsible AI use in engineering
• Experience with building or operating an internal platform, ML platform, or developer experience function (bonus)
• Experience with high‑throughput, low‑latency systems in regulated/financial contexts (bonus)
• Exposure to ISO 27001 / SOC2 and what they demand for infrastructure and processes (bonus)

• Credible with senior engineers and able to facilitate decisions without being the hands‑on expert
• Curiosity about AI, agents, and evaluation methods
• Honest and pragmatic about timelines and trade‑offs
• Cloud infrastructure
• IaC (infrastructure as code)
• CI/CD pipelines