Platform Engineer, AI Agent Systems

Zanskar•Salt Lake City, UT

1d•Hybrid

About The Position

At Zanskar, we're using better technology to find and develop new geothermal resources in order to make geothermal a cheap and vital contributor to a carbon-free electrical grid. Zanskar uses proprietary geophysical models and subsurface data to find geothermal resources faster than anyone else. We’re building the infrastructure that lets our team put AI agents to work on that data — safely. We need a Platform Engineer to own that infrastructure: the systems that let agents operate on sensitive company data and models without creating pathways for that data to be deleted, altered, or exfiltrated. The work isn’t theoretical — we have proprietary subsurface models and geophysical datasets that cannot leave our environment, and we need agents that can work with them in production today. Success in this role centers on two tightly related workstreams that share the same hard underlying problem: letting agents operate on sensitive company data and models safely. You'll design and operate the production systems that run agentic workflows inside our infrastructure as well as support a self-service layer for our internal teams to deploy agent-based tools and agent-built internal apps. You'll own the access control architecture that governs what agents can read, write, and call in both production and sandboxed experiments — explicit trust boundaries, revocable credentials, and audit trails that hold up under scrutiny. Throughout, you'll partner directly with scientists, engineers, finance, legal, and comms to understand what they need, what they'll accidentally break, and how to make the on-ramp fast without compromising the guardrails. Within six months, you'll have foundational agent-deployment infrastructure running in production, a sandbox environment that internal teams are actively using to build tools, and an access-control and observability model that lets Zanskar expand agent usage safely as the company grows.

Requirements

3+ years building and operating internal APIs, platform services, or backend infrastructure.
Experience with containerized environments.
Understanding of service-to-service auth and secrets management.
Knowledge of observability requirements.
Experience working with LLM tool-use or function-calling in a production or near-production context.
Understanding of issues like hallucinated tool calls, unbounded loops, and context bleed.
Hands-on experience with at least one agentic framework and its operational tradeoffs.
Designing systems where the path of least resistance is also the safe path.
Experience considering what happens when a component is compromised.
Ability to figure out the right approach for complex, novel problems.

Nice To Haves

Experience with multi-tenant systems where isolation between environments is a hard requirement.
Familiarity with model serving infrastructure and patterns for proprietary models.
Experience with data access patterns in scientific or geospatial contexts (raster/vector data, large file stores).
Experience managing LLM provider integrations at the API layer (cost observability, rate limiting, failover logic, gateway tooling).
Familiarity with or interest in frontend development.
Experience in Python, TypeScript, Golang, Pulumi, or Kubernetes.

Responsibilities

Design and operate production systems that run agentic workflows.
Support a self-service layer for internal teams to deploy agent-based tools and agent-built internal apps.
Own the access control architecture that governs what agents can read, write, and call in both production and sandboxed experiments.
Partner with scientists, engineers, finance, legal, and comms to understand their needs and implement appropriate guardrails.