Senior Platform Engineer (AI Native)

Summary
Zencargo is looking for a Senior Platform Engineer to support the architectural direction of our cloud platform as our traffic and our AI workloads grow, and to be the team's technical backstop when the hard problems land.

This is a hands-on and broad role. Roughly 60% is infrastructure engineering: cloud architecture, a private Kubernetes estate, an event-driven backbone, and the infrastructure as code that describes all of it. Roughly 40% is building software, from our internal AI tooling and platform services to product engineering.

You will collaborate closely with our forward deployed engineers and infrastructure team, owning solutions and driving outcomes that solve real business needs in a range of approaches from PoC work to long running projects.

Key Responsibilities

• Lead the design, implementation and delivery of complex infrastructure projects.
• Build and scale the infrastructure behind our AI workloads, including cost, API reliability and platform support for LLM-powered features.
• Debug the hard problems: cluster behaviour under load, service-to-service traffic, consumer lag on the event backbone, and database performance.
• Own the infrastructure-as-code estate, keeping drift visible and reconciled, and setting module standards the rest of the team builds against.
• Advance the delivery pipeline, including policy-as-code gates, progressive delivery, and changes that are tested, reviewed and auditable.
• Contribute to our internal AI tooling, including the MCP server and agentic workflows that remove operational toil.
• Engineer cost controls through right-sizing, reserved capacity and waste removal, measured against a baseline and advocate for targeted spend.
• Mentor peers through pairing, code review and knowledge sharing, growing their judgement rather than answering for them.
• Cover security and governance alongside the platform work in collaboration with the infrastructure team.

How success will be measured
Success in this role will be measured by the outcomes delivered, including:

• Reduced time and manual effort in operational workflows, and improved speed, accuracy, cost or service quality across the business.
• Platform reliability and incident outcomes, including systemic fixes landed rather than repeat incidents patched.
• Complex infrastructure projects delivered end to end, with the decisions recorded and followable.
• Infrastructure-as-code and pipeline health, including drift reconciled and standards adopted by the wider team

What we are looking for

• Strong software engineering. You write code you would be happy to maintain, in at least one language, and you can move between languages when the problem requires it.
• Real production infrastructure ownership. You have been responsible for something running in production, on a major cloud, including when it broke.
• Infrastructure as code depth. You understand state, module design, and why reviewed, repeatable changes matter.
• Kubernetes in production. You have debugged a cluster that was misbehaving, not only deployed to a healthy one.
• CI/CD fluency. You have designed a pipeline, not only consumed one.
• Fluent, critical use of AI tooling. We build with AI but understand its limitations too and you will evolve with the tools as the industry transitions.
• You work well remotely - we’re fully remote with offices in multiple locations and we have an async-first mentality.
• Calm under pressure. When the platform is down and the company is watching, you communicate a plan and work the problem methodically, keeping people informed without being in the room.

Desirable Experience

• Experience in freight, logistics or supply chain, or in another B2B SaaS or operational technology environment.
• Experience building workflow automation or orchestration, for example with tools such as n8n.
• Experience our architectural setup e.g. with a service mesh, event streaming at scale or managed Postgres-compatible databases under real load.
• Familiarity with infra-adjacent processes such as Identity and access management as a discipline, including SSO and secret management or SRE methodology.
• Building with LLM APIs, MCP servers or agentic tooling, rather than only using a chat assistant.