Platform / DevOps Engineer, Client Embedded Pod (Kubernetes / Observability / Data Infrastructure)

• *Experience:** 5\+ years, Mid\-Senior Level **Positions / Openings:** 5

• *About the role**
• -----------------

You will join an embedded pod building the platform foundations that product and engineering teams run on, directly inside a client environment on live production systems. This is not a maintenance role. In a single quarter you could be hardening Kubernetes cluster lifecycle and capacity, tuning ClickHouse for high\-cardinality observability, and standing up or scaling Milvus for a retrieval or agent workload. The pod moves fast and the client expects production\-grade infrastructure, not one\-off scripts.

• *What we expect from you**
• --------------------------

• You write clean, production\-ready infrastructure and platform code, and you ship it fast. You do not need heavy process to move.
• You pick up unfamiliar stacks and tools quickly and without much hand holding – Kubernetes operators, GitOps, telemetry pipelines, columnar and vector databases.
• You already use modern AI tools to design, debug, and operate systems, and can show what that has done to your output.
• You are comfortable with ambiguity and switch context across infrastructure domains – cluster work one week, data\-store tuning the next – without dropping reliability.
• You can hold a technical conversation directly with a client stakeholder, not just with your own team.
• You consistently turn platform work into measurable outcomes: developer velocity, cost control, and uptime.

• *Must\-have skills**
• --------------------

• Kubernetes in production: cluster lifecycle management, Helm charts and operators, capacity planning, and multi\-tenant platform design
• GitOps workflows for infrastructure and application delivery (ArgoCD, Flux, or similar)
• ClickHouse or a comparable columnar store: schema design, ingestion pipelines, and query performance tuning for high\-cardinality observability data (logs, traces, metrics, wide events)
• Vector databases in production – Milvus or similar (Pinecone, Weaviate, pgvector) – operating data planes for retrieval and agent workloads
• Telemetry pipeline design spanning logs, traces, and metrics
• Demonstrated use of AI tools in your engineering workflow, and awareness of how to apply AI capabilities to platform operations
• Git and version control workflows in a team setting (branching, PRs, code review)

• *Good\-to\-have skills**
• ------------------------

• Writing or extending Kubernetes operators and custom controllers
• Experience with complementary observability tooling (Prometheus, Grafana, OpenTelemetry)
• Cloud platform experience – AWS, Azure, or GCP
• CI/CD pipeline ownership, not just exposure
• Cost optimization or capacity forecasting for infrastructure spend
• Exposure to building AI agents or working with LLM APIs
• Comfortable writing infrastructure tests and validation as part of your normal workflow
• Experience working in fast, iterative cycles (agile or otherwise) with shifting priorities
• Prior experience working directly embedded with a client rather than purely internal teams

• *What this role is not**
• ------------------------

• This is not a role for someone who prefers a fixed scope and stable domain. The problem domain changes – cluster hardening one week, ClickHouse tuning the next, Milvus scaling after that.
• This is not an entry\-level supervised role. You will be expected to operate independently within a few weeks of starting.