About Liatrio

At Liatrio, we don't just ship software, we enable real transformation.

We help large enterprises break free from legacy systems and truly enable AI across their organizations. We design and deliver secure, scalable, agentic AI-native platforms that reshape how teams build, deliver, and thrive at scale.

We live this every day by embedding deeply with clients to drive meaningful AI enablement — accelerating modernization, reducing risk, and creating lasting competitive advantage through production-grade AI systems. We use tools like Claude, Cursor, and Codex daily to move faster and deliver better outcomes.

If you're a technical leader who leads with AI and drives lasting change, this is where you belong.

About the Role:

We're looking for a Lead Platform Engineer who owns a portion of the technical scope on a workstream, architects the solutions within it, and leads a small team of engineers to deliver against it. 

You work closely under the guidance of an account lead or architect — you're not setting the overall engagement strategy, but you are designing the solutions, making the day-to-day architecture calls, and being accountable for the technical quality of what your team ships. 

You're in the pipelines, writing IaC, building platform environments, and setting the standard through your own work while keeping the team unblocked and the client informed.

What You'll Do:

Technical Delivery and Oversight

You'll own a defined portion of the technical scope — designing the architecture, doing the work, and leading your team through it. That looks like:

• Architecting Platform solutions within your workstream — designing the approach, documenting tradeoffs, articulating it clearly to clients, and leading your team through implementation

• Building, configuring, and maintaining CI/CD pipelines end-to-end — designing the strategy, choosing the toolchain, and evolving it as the client's delivery maturity grows

• Writing and managing infrastructure as code — Terraform or CloudFormation — including module design, state management, and drift

• Designing, writing, and managing AI Agents and AI tooling at scale or an organization as part of expanding platform capabilities

• Building and deploying cloud-native platform environments — whether that's Kubernetes, managed container services, or other orchestration platforms depending on client environment — getting teams to a place where they can run and operate workloads in production confidently

• Building and implementing observability — metrics, logging, tracing, and alerting that actually tells you something useful when things go wrong

• Building developer platform tooling, automation, and self-service infrastructure that makes engineering teams faster and less dependent on manual work

• Integrating AI tooling into Platform workflows and delivery pipelines where it makes the most impact

• Making day-to-day architecture decisions within your scope and escalating the right ones to the account lead or architect when they have broader implications

• Identifying technical risk early and surfacing it clearly before it becomes a delivery problem

• Generating ideas, failing fast, and demonstrating with POCs

Team and People

• Lead a small team of engineers — organizing work, breaking down tasks, pairing on hard problems, and keeping the team moving

• Uplift engineers through code review, pairing, and day-to-day technical mentorship

• Mentor junior team members through 1:1s, feedback, and guiding technical growth

Stakeholder Communication

• Serve as a technical point of contact for your scope — representing the work clearly to client engineers and project stakeholders

• Keep account leaders and architects up to date on concerns, blockers, and changes

• Adapt quickly to change and shift focus as required to meet customer needs

• Provide feedback on team performance and areas for growth

Experience & Skills:

• You can own a CI/CD pipeline end-to-end — designing the strategy, building it, and evolving it as the client's delivery maturity grows

• You've written and managed production IaC at scale — Terraform or CloudFormation — including module design, remote state, and managing infrastructure drift in a live enterprise environment

• You've built and deployed cloud-native platform environments in real production settings — whether that's Kubernetes, managed container services, or other orchestration platforms — and helped engineering teams get to a place where they can run and operate their workloads confidently

• You've made real architecture decisions — designed systems under constraints, documented tradeoffs, and been accountable for what happened when those decisions met production

• You've built meaningful observability for real systems — you know the difference between metrics that tell a story and dashboards nobody looks at, and you can set up alerting that pages on the right things

• You've built platform tooling and developer infrastructure — self-service tooling, automation, or internal infrastructure that made engineering teams faster and less dependent on manual ops work

• You've worked deeply across at least one major cloud provider and have enough breadth across others to be productive quickly in a new environment

• You can hold the technical bar for a small team — reviewing code, catching architecture problems early, and helping engineers grow through the process

AI and Intelligent Systems

• You've integrated AI capabilities into real delivery workflows — whether that's AI-assisted pipeline troubleshooting, intelligent automation, or tooling that makes your team faster — and you can describe what worked and what didn't

• You use AI coding tools like Claude, Cursor, or Windsurf as a daily driver and actively help your team get value from them

• You're familiar enough with LLM capabilities to make smart decisions about where to apply them in a Platform context