Lumnix

About client

Client builds and operates GPU cloud infrastructure at the scale modern AI demands. We run dense accelerated-compute clusters on bare metal, powered by NVIDIA and AMD accelerators and K8s, stitched together with high-performance Ethernet and InfiniBand fabrics, and we deliver them to customers as reliable, secure, high-throughput platforms. We are scaling rapidly across new data center sites, and the infrastructure team sits at the center of that growth, building the systems that turn racks of hardware into a product customers depend on.

The role

We are hiring a Principal Infrastructure Engineer to build the foundation of our greenfield v2 cloud platform: the bare-metal provisioning and control-plane substrate that turn new racks of GPU hardware into reliable, secure, customer-ready capacity.This is a build-focused, hands-on role and the most senior individual contributor on the platform build. You will own the recommendation and the call on the infrastructure layer (we’re currently in POC across different vendors) and the build-vs-buy decision that goes with it, with our Cloud Platform Architect pressure-testing it. You will drive integration across the other domains and define how infrastructure is templated, multi-tenanted, and handed to customers. You will define the architecture and the standards the rest of the team builds to, while still being deep in the code yourself. Day-to-day operations are not your focus, getting the platform designed and built right is.

You will work at the center of new cluster and new-site buildouts, partnering closely with our network, SRE, and security engineers. They own the fabric and day-to-day operations; you own getting the platform built right. Because the systems you design become the foundation customer GPU workloads run on, getting the architecture and automation right the first time matters more here than almost anywhere.

What you’ll own

• Platform architecture and technical direction: own the architecture of the cloud platform control plane and orchestration layer for new GPU clusters, making the foundational decisions and setting the technical standards the rest of the platform is built on.

• Bare-metal provisioning automation: build the automated pipeline that takes a node from rack-and-stack handoff to a fully configured, real acceptance gates before a node is marked customer-ready, firmware to the qualified recipe, GPU burn-in, NCCL/RCC runs, and fabric checks, plus a golden-image build-and-qualify pipeline in CI repeatedly across sites and at scale.

• Infrastructure as code and automation: treat the platform as code from day one. Build the Terraform and Ansible that provision, configure, and validate the platform, so the build is reproducible rather than hand-assembled.

• Control-plane substrate: build the control plane that turns provisioned bare metal into cluster-ready capacity, and own the clean handoff to the Kubernetes layer. You own tenant isolation at the network and hardware layer; the Kubernetes and namespace isolation model belongs to the Principal Kubernetes Platform Engineer.

• DPU and SmartNIC integration: design how we leverage BlueField DPUs in the platform, offload, provisioning, isolation, and the SuperNIC/DPU-mode configurations that let us get the most out of the hardware.

• Platform reliability by design. Build observability, guardrails, and progressive-rollout mechanisms into the platform from the start. As you build it and design to our SOC 2, ISO 27001, and HIPAA obligation.

• Security Built-in: instrument the platform as you build it and design to our SOC 2, ISO 27001, and HIPAA obligations. Vault-based secrets model from the start. BIOS and system prep for trusted domain execution, secure boot, and backend-fabric hardening.

• Confidential-compute-ready platform (growth area): Design the tenancy and scheduling so it fits our confidential-compute roadmap: whole-GPU per tenant, and TDX with GPU passthrough. Deep CC background is a plus, not a requirement.

• New site buildouts: take a critical role in standing up the platform at new data center sites, turning green-field facilities into production capacity.

• Technical leadership: set patterns, review designs, and raise the engineering bar across the infrastructure team multiplying the impact of other engineers and mentoring senior ICs, without becoming a people manager.

What we’re looking for

Required

• 10+ years building production infrastructure at scale, including deep green-field and platform-build experience, for cloud, hosting, or high-performance compute environments.

• A track record of setting technical direction on complex platforms, architecture others adopt, standards others follow, and influence that extends well beyond your own keyboard.

• Strong Linux systems fundamentals, you can reason about the kernel, networking stack, storage, and performance, not just the command line.

• Fluency in infrastructure as code and automation: Terraform, Ansible, and a real programming language used to build durable platform tooling, not just one-off scripts.

• Production Kubernetes experience: building, deploying, and operating clusters and the platform services that run on them.

• A demonstrated bias toward building things right the first time: sound architecture, reproducible automation, and durable design over short-term shortcuts.

• Strong written communication, you document designs and decisions clearly and leave systems more understandable than you found them.

Strongly preferred

• DPU / SmartNIC experience, especially leveraging offload, DPU/SuperNIC modes, and platform designs that exploit them. This is a meaningful differentiator for us.

• Experience with GPU and accelerated-compute infrastructure across NVIDIA and AMD, including ROCm and firmware lifecycle, or HPC/AI infrastructure.

• Bare-metal provisioning at scale (Redfish/BMC automation, NetBox or a similar source of truth).

• Experience standing up cloud-platform control-plane stacks in a green-field setting.

• Networking depth, BGP, VLANs, fabric concepts, and the ability to debug connectivity and throughput issues. You won’t own the network, but the more fluently you can partner with our network engineers, the better.

• Familiarity with high-performance fabrics: RoCE Ethernet / InfiniBand.

• HashiCorp Vault, and building within a SOC 2 / ISO 27001 / HIPAA compliance posture.

How we work

• Small, senior, high-trust teams. We hire people who can own and solve problems end to end and we give them the room to do it.

• Code over clicks. The platform is defined, reviewed, and versioned. Manual changes are the exception and they get automated away.

• Build it right. We invest in sound architecture and durable design so the platform scales cleanly instead of accumulating debt.

• Customers are real. The platform exists to serve customer workloads, and we build to the reliability and white-glove standard that implies.

• We document. Designs, decisions, and architecture are written down so the team compounds knowledge instead of re-learning it.

AI as a force multiplier

We expect our engineers to use AI to move faster and operate at higher leverage, and we build our environment to make that the default rather than an afterthought. We maintain CLAUDE.md and AGENTS.md context files across our repositories, run MCP servers against our operational systems (Jira, NetBox, Checkmk), and use agentic coding tools such as Claude Code directly in our infrastructure and platform workflows.

You don’t need to be an AI expert, but you should be eager to fold these tools into how you design, build, and automate — and to help us push the frontier of what an AI-leveraged infrastructure team can do.

What success looks like

First 30 days

• Ramped on our environment: the platform stack, provisioning approach, and target architecture. able to navigate the codebase and design docs independently.

• Shipped your first meaningful contribution to the platform build with the team’s review.

• Aligned with network, SRE, and security on where your work meets theirs.

First 90 days

• Owning a defined component of the platform build end to end, from design through delivery.

• Delivered a meaningful piece of provisioning or platform automation that made the build faster, more reproducible, or more reliable.

• Shaped at least one foundational architecture decision that the platform is built on.

First 12 months

• The recognized technical authority for the cloud platform build, setting direction and trusted across network, SRE, security, and platform engineering.

• Designed and delivered a significant piece of the cloud platform or its automation that raised the team’s leverage and the platform’s reliability.

• Took a lead role in bringing at least one new data center site online.

About the client
The client is a neocloud building purpose-built GPU infrastructure for AI workloads. We operate large-scale clusters powering training and inference for some of the most demanding AI customers in the market, and we’re rapidly expanding. Our infrastructure runs on NVIDIA and AMD accelerators, InfiniBand and high-speed Ethernet fabrics, and a production stack spanning bare-metal provisioning, Kubernetes, and OpenStack.

We’re small, senior, and moving fast. The people who do well at the client own problems end-to-end and make decisions with incomplete information.

The role
We are hiring an Incident & Reliability Lead to own the incident and problem management programs at the client. This is the person who runs the call when a customer cluster goes down, drives the post-mortem afterward, and hardens the runbooks so the same failure does not surface twice.

This is not an ITIL paperwork role. You will be technically credible across Kubernetes, Linux, networking, and GPU infrastructure, enough to lead a live incident with platform engineers, and a customer on the bridge. But your center of gravity is the process: fast, clean incident response, honest communication, and a relentless feedback loop between customers engineering.

You will set the standard for how the client behaves during our most challenging hours. Done well, this is one of the most visible and highest-leverage roles on the team.

You will work closely with cloud operations, network teams, and customer-facing counterparts. Expect to be on-call in a shared rotation.

What you’ll own

• Incident command. Run major incidents end-to-end: triage, escalation, comms cadence, timeline, decision log, and drive-to-resolution. You are the conductor, not the fixer.

• Customer communications. Own customer-facing incident messaging, initial acknowledgement, status updates at defined intervals, resolution notice, and written RCA. Clear, honest, no hedging.

• Post-mortem and RCA program. Run blameless post-mortems, write the RCA, track action items to completion, and publish learnings across the org. Own the quality bar for RCA writing at the client.

• Escalation framework. Define and maintain the severity matrix, escalation trees, on-call rotations, and paging policies across platform, network, DC ops, and leadership.

• Runbooks and playbooks. Turn tribal knowledge into durable, testable runbooks. Drive the engineering teams to document before the next 2am page, not after.

• On-call health. Own the on-call experience: page volume, alert quality, rotation fairness, and handoff hygiene. Kill noisy alerts. Fix the ones that matter.

• Reliability metrics. Define and report the metrics that matter: MTTA, MTTR, incident count by class, SLO attainment, repeat-offender systems. Make reliability a number the business trusts.

• Game days and drills. Plan and run incident drills across clusters, regions, and customer scenarios. Surface gaps before customers do.

• Incident tooling. Own the incident tooling stack, paging, status page, incident channel automation, timeline capture, RCA templates. Evaluate and land the right tools.

What we’re looking for

Required

• 7+ years in SRE, production engineering, or infrastructure operations, with clear ownership of incident management in at least one prior role.

• Proven ability to run major incidents as the commander, keeping a room calm, driving decisions, managing comms to customers and executives in parallel.

• Strong technical fluency across Linux, Kubernetes, networking (L2/L3, BGP basics), and cloud or bare-metal infrastructure. Deep enough in the stack to challenge assumptions, debug alongside engineers on a bridge call, and senior enough to turn each incident into durable improvements like runbooks, automation, guardrails, so the next incident does not need you.

• Excellent written communication. You can draft a customer-facing RCA that is accurate, clear, and does not over- or under-promise, and do it within the SLA window.

• Experience owning SLOs, error budgets, or equivalent reliability metrics as a working practice, not a slide.

• Experience designing on-call rotations and alerting practices across multiple teams.

• Comfortable operating in a fast-moving, still-forming environment. You write the runbook that did not exist yesterday.

Strongly preferred

• Experience in GPU infrastructure, HPC, or AI/ML platform operations, NCCL, InfiniBand, DCGM, or similar.

• Direct experience with Kubernetes incident response, control plane, CNI, operator patterns, noisy-neighbor debugging.

• Experience interfacing with neocloud, enterprise or hyperscaler customers during live incidents.

• Familiarity with Prometheus, Checkmk, Grafana, and modern incident tooling (OpsGenie/JIRA).

• Prior experience at a neocloud, CSP, or infrastructure vendor where downtime had direct revenue impact.

How we work

• We’re remote-first across US, LATAM, and EU time zones with a strong operational culture.

• We use AI tooling heavily in day-to-day operations and expect everyone on the team to be fluent with it and to help us get more leverage from it.

• We ship, we debug, we iterate. We don’t process-engineer our way around problems that need to be solved.

• We value written artifacts, RCAs, runbooks, design docs, over meetings. We value calm over performative urgency. We protect focus time and we protect sleep.

AI as a force multiplier

The client runs the infrastructure that powers the AI economy. We expect our team to use AI aggressively in how we work. For this role specifically, that means using AI to draft RCAs faster, triage logs and metrics during incidents, summarize timelines, generate customer-facing communications from raw incident data, and turn post-mortems into runbook updates automatically. If you have not yet built this into your workflow, you will here.

What success looks like

First 30 days

• Shadowed on-call rotations across the platform, network, and ops teams.

• Read every RCA written and identify the top three recurring failure classes.

• Met with the top three customers and understood their incident expectations and SLA terms.

• Documented the current-state incident response process, gaps and all.

First 90 days

• Published a revised severity matrix, escalation tree, and customer-comms cadence, adopted across teams.

• Run at least two major incidents as commander with clean timelines and customer comms.

• Delivered a new RCA template and coached the team through at least three post-mortems against it.

• Stood up baseline reliability metrics (MTTA, MTTR, incident count by class) with a weekly reporting cadence.

• Killed at least 20% of current alert volume by eliminating low-signal pages.

First 12 months

• The client has a mature, documented incident management program that a new customer can diligence with confidence.

• Repeat-offender incident classes are down materially, with clear attribution to runbook, tooling, or engineering changes you drove.

• Customer-facing RCAs are consistently delivered within SLA and are referenced by customers as a differentiator.

• On-call health (page volume, rotation fairness, engineer-reported burden) has measurably improved.

• Game days are a regular cadence and have surfaced and closed real gaps before they became customer incidents.

About the Client

Our client is a neocloud building purpose-built GPU infrastructure for AI workloads. We operate large-scale clusters powering training and inference for some of the most demanding AI customers in the market, and we’re rapidly expanding. Our infrastructure runs on NVIDIA and AMD accelerators, InfiniBand and high-speed Ethernet fabrics, and a production stack spanning bare-metal provisioning, Kubernetes, and OpenStack.

We’re small, senior, and moving fast. The people who do well here own problems end-to-end and make decisions with incomplete information.
 

The role

Security is a core part of who we are and how we operate. We’re hiring a Senior Production Security Engineer to help strengthen and scale our security capabilities.

This is not a compliance-focused role, and it is not purely advisory. We are looking for a hands-on senior engineer who can identify high-risk gaps and opportunities, partner with engineering and leadership teams to design effective solutions, and implement those solutions through code, automation, and tooling. In addition to security-focused initiatives, this person will contribute meaningfully to broader infrastructure, platform, and production engineering efforts.
Beyond the technical responsibilities, this role plays a key part in fostering a security-first engineering culture. You will advocate for best practices, educate teams, and help raise the security bar across the organization. This is an opportunity to shape the future of our security function and become a foundational leader as it continues to grow.
 

What this role is, and is not

This role is:

• A senior engineer who drives the core of a security focus.
• A self-directed practitioner who identifies security weaknesses, opportunities to level up, prioritizes them, and builds a credible roadmap.
• A cross-functional partner who works with infrastructure, platform, cloud, and leadership to recommend action and architect solutions.
• A builder who implements solutions in software, tooling, and infrastructure, not just slides and policies.
• A reviewer and advisor who partners with developers and teams on proposed projects from a security perspective.
• A versatile contributor who still gets meaningful work done in other engineering domains.

This role is not:

• A regulatory or legal compliance role. Compliance evidence will be a byproduct of good engineering, not the job itself.
• An audit-and-recommend-only role. We need someone who builds what they recommend.
   

What you’ll work on

The scope below outlines where this role will harden, automate, and validate Client’s security posture across our infrastructure, platform and customer facing surfaces.  We expect the person in this role to refine priorities based on what they learn in their first weeks.
 

Internal tools, services, and applications that harden our security posture

• Proper network segmentation best practices across tenants, management, and customer planes.
• SSH and system access management architecture
• Multi-tier secret storage for both internal users and customers.
• User account management and lifecycle policy for core systems (joiners, movers, leavers done right).
• Secret rotation pathways for core internal systems, automated, auditable, and not reliant on tribal memory.
• x509 certificate issuing architecture, an internal PKI we can actually operate and rotate.
   

Security-focused monitoring, alerting, and auditing

• Multi-redundancy Vault admin logging.
• GCP access and action logging with real retention and alerting.
• Automatic alerting and reporting from log aggregation pipelines.
• Network traffic analysis and reporting, feeding off our existing fabric telemetry.
• Security-focused dashboards that are actually used, not just built.
   

Customer-facing secret and identity management

As our cloud offering matures, this role helps define the customer-side story:

• KMS architecture and options.
• Encryption strategy, at rest, in transit, and tenant-scoped.
• Security reporting surfaces for customers.
• Customer login, identity, and account management for the cloud platform.
   

Review of what we already have

• Own and drive third-party penetration tests and red team engagements.
• Lead a full design and architecture review of our current stack and document the risk landscape.
• Translate findings into a prioritized, resourced remediation plan.
   

Clear policies and partnerships around third-party code

• Own the liability model, or make the deliberate decision to pay someone else to.
• Processes and tooling for third-party software review, and dependency risk.
• A “happy path” for engineers, the easy, supported way to pull in third-party code safely.
   

What we’re looking for

Required

• 7+ years of hands-on engineering experience, with a substantial portion focused on security in production environments.
• Demonstrated experience designing and operating in multi-tenant, tenant-isolated environments, cloud, neocloud, hosting, or regulated enterprise.
• Deep, practical familiarity with secrets management (HashiCorp Vault or equivalent), PKI, and identity systems (Okta, Google Workspace, or similar).
• Strong Linux and OS fundamentals, you understand what is actually happening on the box, not just what the tool reports.
• Working fluency in networking and network security: firewalls, segmentation, VPN, BGP at a conceptual level, and modern L3/L4 controls.
• Experience with at least one of: Kubernetes security (admission control, network policy, workload identity), cloud IAM at depth (GCP, AWS, or Azure), or infrastructure-as-code security review.
• CI/CD automation design and implementation experience, enough to build the paved road yourself when needed.
• Experience driving or responding to a third-party audit, penetration test, or customer-led security review.

• Clear, direct written communication, much of our team and work is async.

   

Strongly preferred

• Experience being the first or one of the first dedicated security hires at a company, and building a function from there.
• Experience in GPU cluster, HPC, or AI infrastructure environments.
• Familiarity with InfiniBand, RoCE, or other high-performance fabrics from a security and segmentation perspective.
• Experience with SOC 2, ISO 27001, HIPAA, or similar frameworks, enough to know what auditors actually care about, without being a compliance specialist.
• Experience supporting sensitive customer workloads (regulated industries, government, frontier AI labs).

How we work

• We’re remote-first across US, LATAM, and EU time zones.
• We write things down, decisions, architecture, runbooks, post-mortems, threat models.
• We use AI tooling heavily in day-to-day operations and expect everyone on the team to be fluent with it and to help us get more leverage from it.
• We ship, we debug, we iterate. We don’t process-engineer our way around problems that need to be solved.

AI as a force multiplier

We’re making a deliberate bet that AI changes the shape of infrastructure and security teams. Our plan is to scale Client's platform faster than we scale headcount, using coding agents to write and refactor automation, AI-assisted testing and validation to harden our changes, and LLM-driven tooling to compress the work of investigation, documentation, and operational review. We’re already doing this in production: Claude Code for SRE operations, MCP servers exposing Jira, NetBox, and Checkmk, and an active effort to turn tribal infrastructure knowledge into AI-consumable formats.

For security specifically, we want someone who is excited about this, not someone who tolerates AI tooling because it’s in the JD. That means:

• Using AI-assisted workflows as the default for tasks like log review, config audit, threat modeling support, and policy drafting.
• Identifying where agents can own meaningful slices of security work, triaging alerts, drafting RCAs, generating and validating hardening configurations, reviewing pull requests for security-relevant changes.
• Building the substrate the team needs to make AI actually useful, good documentation, clear schemas, MCP integrations, and structured knowledge about our environment.
• Being honest about where AI is adding value and where it isn’t, with the team and with leadership.

If your reaction to this is “finally, a team that’s serious about this,” we should talk.

What success looks like

First 30 days: You’ve built working relationships with the infrastructure, platform, network, and cloud teams, shadowed enough of our production environment to understand where real risk sits, and produced a draft of our top security issues ranked by impact and effort.

First 90 days: You own a credible, prioritized security risk register that leadership and engineering actually use in planning. You’ve made at least one meaningful architectural call, on access, secrets, or identity, that the team agrees was the right one. Shared admin-scoped SSH keys and shared or weak passwords on critical systems have a concrete plan and timeline to be eliminated.

First 12 months: A functioning internal PKI and secret rotation story is in place for core systems. Security-relevant logging, alerting, and dashboards cover our highest-value systems with an on-call path that works. A third-party penetration test has been executed and findings have a remediation plan with owners and dates. Engineering teams have a clear, low-friction path for third-party code and for new service design reviews. You and leadership have a shared point of view on what the next hire into the security function looks like, and whether this becomes a team, a CSO search, or both.

Benefits:

• Unlimited Paid Time Off (PTO)
• Zero-cost employer-covered health insurance
• Company-funded 401k contributions
• Transparent, candid culture with 1:1 coaching, performance reviews, and a consistent feedback loop
• Diverse, respectful, high-performing coworkers you’ll want to achieve greatness with!  

About the Client

Our client is a neocloud building purpose-built GPU infrastructure for AI workloads. We operate large-scale clusters powering training and inference for some of the most demanding AI customers in the market, and we're rapidly expanding. Our infrastructure runs on NVIDIA and AMD accelerators, InfiniBand and high-speed Ethernet fabrics, and a production stack spanning bare-metal provisioning, Kubernetes, and OpenStack.

We're small, senior, and moving fast. The people who do well here own problems end-to-end and make decisions with incomplete information.
 

The role

We're hiring a Senior Manager, Platform Engineering to lead the team that keeps our GPU

clusters running and evolving. You'll own the operational heartbeat of Company's cloud platform, the people, the systems, and the practices that turn racks of GPUs into reliable customer-facing infrastructure.

This is a hands-on leadership role. You'll start roughly 50/50 between technical work and management as you ramp up, earn context, and build trust with the team, and scale toward 80/20 management over your first 12 months as the team grows and your direct reports take on more. We're not looking for a manager who has forgotten how to read a stack trace, and we're not looking for a senior IC with a team tacked on. We're looking for someone who leads by setting technical direction, raising the bar on operational rigor, and growing engineers, and who can still jump into an incident bridge and be useful.
 

What you'll own

• A team of ~9 engineers spanning production engineering, SRE, security, and

automation.

• The reliability, performance, and operability of Client's GPU cloud platform across

multiple clusters and customers

• Incident response and post-incident culture, you'll set the standard for how we

investigate, communicate, and learn from outages

• Operational readiness for new clusters and data center buildouts, in close partnership

with our DC Build, Networking, and Program Management functions

• Platform automation and infrastructure-as-code maturity, reducing toil, codifying tribal

knowledge, and making our environment legible to both engineers and AI tooling

• Hiring, coaching, and career development for the team, including an anticipated split of the team into specialized functions as we scale
   

What we're looking for

Required

• 8+ years of infrastructure, production engineering, or SRE experience, including 3+

years managing or tech-leading engineers

• Deep hands-on experience with Linux production systems at scale, you've debugged

kernel, networking, and storage issues in anger, not just read about them

• Strong Kubernetes operational experience, you understand what breaks in Kubernetes

at scale and why, not just how to write a deployment manifest

• Experience running cloud or cloud-adjacent platforms in production, IaaS, bare-metal, or hybrid, with real customers depending on uptime
• Fluency with modern automation and IaC tooling (Ansible, Terraform, or equivalent) and a bias toward codifying operational knowledge rather than keeping it in people's heads
• Track record of building and running on-call, incident response, and post-mortem

practices that engineers actually trust

• Clear, direct written communication, much of our team and work is async
  
   

Strongly preferred

• Experience operating GPU or HPC infrastructure, or a strong appetite to go deep on it

quickly

• OpenStack, or bare-metal provisioning experience
• Experience working alongside networking and security engineers as peers, not as tickets to file
• Familiarity with observability stacks (Prometheus, Grafana, Checkmk, or similar) and a

point of view on what good monitoring looks like

• Experience scaling a team through rapid growth, splitting functions, hiring against a plan, and evolving reporting structures without breaking trust
   

How we work

• We're remote-first across US, LATAM, and EU time zones
• We write things down, decisions, architecture, runbooks, post-mortems
• We use AI tooling heavily in day-to-day operations and expect everyone on the team to be fluent with it and to help us get more leverage from it
• We ship, we debug, we iterate. We don't process-engineer our way around problems that need to be solved
   

AI as a force multiplier

We're making a deliberate bet that AI changes the shape of infrastructure teams. Our plan is to scale our platform faster than we scale headcount, using AI-powered development tools to build and refactor automation, AI-assisted testing and validation to improve reliability, and LLM-driven workflows to accelerate investigation, documentation, and operational review.

We're already applying these approaches in production and actively working to turn institutional knowledge into AI-accessible systems that improve operational efficiency and decision-making.
We want a leader who is genuinely excited about this, not someone who tolerates AI tooling because it's in the JD, but someone who will drive it. That means:

• Setting the expectation on the team that AI-assisted workflows are the default, not the exception
• Identifying where agents can own meaningful slices of work, writing playbooks,

generating tests, validating configurations, triaging alerts, drafting RCAs

• Building the substrate the team needs: good documentation, clear schemas, MCP

integrations, and the kind of structured knowledge that makes AI actually useful rather

than a gimmick

• Measuring what's working, killing what isn't, and being honest with the team and with

leadership about both

If your reaction to this is "finally, a team that's serious about this," we should talk.
 

What success looks like

First 30 days: You've built 1:1 relationships with every member of the team, shadowed enough incidents and customer escalations to have a real picture of where we are, and identified the two or three operational patterns most worth changing.

First 90 days: You're the owner of our incident and post-mortem practice. You've onboarded two new senior hires (SRE and Security Engineer) and set clear expectations for their first deliverables. You've made at least one meaningful call on platform architecture or operational tooling that the team agrees was the right one.

First 12 months: The team is measurably more effective, fewer repeat incidents, faster time to resolution, more work automated away. You've hired at least one additional engineer, coached at least one existing team member into a stretch role, and established Platform Engineering as a function that other teams at Company want to work with.