We are seeking a Senior Platform Engineer (AI Systems) to join our Core Team.

Strike is already a high-velocity, cloud-native engineering organization. We run immutable infrastructure on GKE, we enforce "Shift-Left" security, and we deploy constantly. We are fully integrating frontier AI capabilities directly into our ecosystem — not as a gimmick, but as a force multiplier for our team.

This is a hands-on Systems Engineering role focused on building, shipping, and operating. You will not be fine-tuning models or building chatbots for fun. You will build and run the secure connective tissue that lets agentic systems interact with our real-world infrastructure: how agents authenticate, how they access tools, and — crucially — when they should be trusted. The AI platform direction is set; your job is to turn it into production systems that our enginners relies on every day.

Who this is NOT: To save your time — this is NOT a Data Science or MLOps role. We are not looking for someone to train custom models in PyTorch or manage Kubeflow clusters. We are looking for a systems builder to ship and operate the agentic harness and tools that empower our team.

What You Will Do

• Build and operate the internal AI platform. We have already operationalized tools like Copilot Agents, Gemini, Claude, agentic CLIs, n8n workflows, and local agent runtimes. You will build and run the unified, secure layer that connects these tools to our core platforms — including building and hosting internal MCP servers on our GKE / Helm / ArgoCD stack.
• Ship IDE agents. Deploy, configure, and tune IDE agents that accelerate coding for the entire engineering organization (harness / prompt / config work — not model training).
• Build agentic workflows. Create and run the orchestration layers and secure runtime environments for LLM tool-use.
• Bridge information silos. Build out the enterprise knowledge base and connectors that let agents reason across disparate data sources.
• Implement Tiered Autonomy. Build the security patterns for different levels of agent independence — from "Advisory" (read-only) to "Autonomous" (action-taking) — and implement the "Verifiable State" and "Revert" mechanisms that make Tier 3 autonomy safe.
• Secure the frontier. Implement the auth patterns for agentic access: authentication, authorization, and "human-in-the-loop" safeguards that let us move fast without breaking our "Zero Trust" principles. Deliver it as code, through our existing IaC and GitOps pipelines.
• Build pragmatically. You know when to use an LLM and when to just write a script. You help teams identify "Units of Toil" and target them with the right level of abstraction.
• Force-multiply the team. Embed with teams to unblock their workflows hands-on. You are the go-to expert on frontier tools, helping engineers optimize their setups and adopt "AI Native" workflows.

What We Are Looking For

• Hands-on with our kind of infrastructure. You have personally built, deployed, and operated production systems on Kubernetes (ideally GKE) with Helm and GitOps (ArgoCD), managed cloud infrastructure as code (Terraform or equivalent) on GCP/AWS/Azure, and worked with enterprise secrets management. You have carried the pager for what you shipped — you don't just use this stack, you run it.
• Full-Stack Systems DNA. You are a polyglot engineer comfortable across the stack — frontend interfaces, backend distributed systems, and cloud infrastructure. Bonus for C#/.NET, Python, and TypeScript.
• Frontier AI Proficiency. You are fluent in the current landscape of AI enablement and have shipped implementations using patterns like MCP, RAG, function-calling, and evals. You understand techniques like AI-as-a-Judge and agent self-improvement, and how to balance deterministic systems with non-deterministic outputs.
• Security-First Mindset. You understand the risks of deploying LLMs in production. You are fluent in Shift-Left security, least-privilege access for non-human actors, and robust secrets management.
• Pragmatism. You treat AI as a component, not magic. You are skeptical of hype and focused on measurable utility, and you balance the cognitive load of LLM output against the efficiency it buys.
• Ownership. You are an owner at heart. Taking ownership of your domain is innate and automatic. You operate autonomously, do the unglamorous 80%, and see things through to completion — every time.
• Clear communicator. You can explain complex AI concepts to non-technical stakeholders and collaborate closely with the teams you embed in to drive real adoption.

Our Environment (Context)

• Infrastructure: Fully cloud-native on GCP, Infrastructure as Code (Terraform).
• Orchestration: Google Kubernetes Engine (GKE) with Helm & ArgoCD.
• Languages: C#/.NET (core services), Python (data/scripts), TypeScript (web).
• Security: Comprehensive enterprise secrets management and governance tooling.
• AI Stack: We use a mixture of tools and models; being flexible and ready for change is the default.

US-Based Positions

• Salary range: $206K - $232K
• Equity in a high-growth startup
• Health, dental, and vision insurance premium contributions; short & long-term disability insurance and basic life insurance
• Cell phone and internet reimbursement
• Flexible PTO, sick leave & parental leave
• Access to a company 401k plan
• No trading fees when you buy and sell bitcoin on Strike

Non US-Based positions

• Benefits and compensation are location dependent

We do not make hiring decisions based on educational history whatsoever. Our Founder is a college dropout. We work with high school dropouts, PHD candidates and everything in-between. We do not hire credentials. We simply partner with talented, passionate individuals who are excited to be a part of our team.

By clicking submit application below, you consent to our use and processing of your data as described in our Candidate Privacy Notice.