Moss
SaaS
SeniorPlatformEngineer(AgenticExperience)(f/m/d)
Neural analysis suggests this role is
optimal for Senior candidates.
“Senior Platform Engineer (Agentic Experience) (f/m/d) at Moss. Skills: Platform Engineering, Developer Experience, Agentic AI Tools, Kubernetes Management, Microservices Architecture. Build a developer productivity measurement platform integrating GitHub, Linear, Slack, and CI/CD signals into a coherent view of delivery flow. Identify and remove friction from engineering workflows: slow pipelines, poor feedback loops, unclear ownership, and noisy alerting”
What You'll Achieve.
turn that investment into measurable outcomes; help engineers across the company move faster and more safely with AI; drive improvements with their buy-in
Industry & Context.
identifies what needs solving, not someone who waits for a Jira ticket; Comfortable navigating ambiguity and making decisions as new information emerges, without waiting for the problem to be fully defined; Able to identify what needs solving, not just execute what has been scoped
What They're Looking For.
Must Have
8+ years of experience in cloud-native startups, FinTechs, or high-volume engineering environments, 3+ years working in a team focused on developer experience, 1-2 years using modern agentic tools in a professional setting, 3+ years of hands-on Kubernetes experience or managing Cloud infrastructure, 5+ years working with microservices architecture, Business fluent in English (C1 or above)
What You'll Do.
Build a developer productivity measurement platform integrating GitHub
and CI/CD signals into a coherent view of delivery flow
Identify and remove friction from engineering workflows: slow pipelines
and iterate on AI tooling for local development
including IDE integrations
and model integrations such as Claude and Codex
Build the guardrails that make AI adoption safe: prompt logging
data classification boundaries
an approved model catalogue
and usage observability
Understand the workflows of engineering and product teams to surface bottlenecks
and drive improvements with their buy-in
Maintain Kubernetes and microservices credibility across the platform
Write documentation that engineers actually use: runbooks
How You'll Work.
Team & Collaboration
working alongside Platform, Data, and AI Engineers; Understand the workflows of engineering and product teams to surface bottlenecks, systemic issues, and drive improvements with their buy-in; Builds trust with engineering teams through hands-on presence, not top-down mandates
Communication Scope
Business fluent in English (C1 or above)
Full Job Description
At Moss, we give finance professionals the power to automate their day-to-day and make forward-thinking decisions. Our team and culture make us unique — we’re driven by impact and growth, where every one of us strives to learn and excel. Recognised by Sifted’s Rising 100 https://sifted.eu/rankings/b2b-saas-rising-100-2024 and LinkedIn's Top Startups https://www.linkedin.com/pulse/linkedin-top-startups-2024-20-aufstrebende-unternehmen-bjd0c/, we’re here to help propel your career and together, make Moss a lasting success. Moss is making a significant investment in AI tooling - and this role exists to turn that investment into measurable outcomes. As the first dedicated hire focused on agentic developer experience, you will build the tools, modular skills, guardrails, and infrastructure that help engineers across the company move faster and more safely with AI. You will report to the Director of Engineering and sit within a Platform Engineering team of around eight people, working alongside Platform, Data, and AI Engineers. The problems here are rarely well-defined upfront - this role is for someone who identifies what needs solving, not someone who waits for a Jira ticket. Your responsibilities - Build a developer productivity measurement platform integrating GitHub, Linear, Slack, and CI/CD signals into a coherent view of delivery flow - Identify and remove friction from engineering workflows: slow pipelines, poor feedback loops, unclear ownership, and noisy alerting - Define, deploy, and iterate on AI tooling for local development, including IDE integrations, agentic tools, MCP servers, and model integrations such as Claude and Codex - Build the guardrails that make AI adoption safe: prompt logging, data classification boundaries, an approved model catalogue, and usage observability - Understand the workflows of engineering and product teams to surface bottlenecks, systemic issues, and drive improvements with their buy-in. - Maintain Kubernetes and microservices cr
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