AI Platform Engineer

Productionizing AI workflows: Transform prototypes into robust production systems with defined service-level agreements (SLAs), supporting use cases such as specification-to-pull-request agents, AI-powered code reviews, incident triage, and Know Your Business (KYB) reviews.

Building and managing MCP servers: Design, deploy, secure, and maintain Model Context Protocol servers, including authentication, per-user OAuth, scoped access, and integrations with tools such as Slack, GitHub, Jira, HubSpot, and internal services.

Developing autonomous agents: Build multi-step agents capable of planning and taking action across different tools, progressing through controlled trust stages from shadowing and suggesting to executing tasks autonomously.

Implementing security and governance: Establish platform-wide guardrails, including agent identities, scoped credentials, audit logs, CI checks, approval workflows, allowlists, and least-privilege access policies.

Creating evaluation frameworks: Develop test datasets, evaluation criteria, regression suites, and live production scoring systems to measure agent quality and connect performance metrics to business outcomes.

Improving observability: Instrument agents and workflows with tracing, logging, latency and cost metrics, drift detection, alerts, and dashboards that help internal Operators monitor performance.

Managing AI expenditure: Track usage and spending across AI providers, including Anthropic, OpenAI, and OpenRouter. Establish budgets, alerts, and cost attribution at the team, agent, and workflow levels.

Supporting internal stakeholders: Work directly with Operators across business functions to understand their needs, develop requested features, create documentation, deliver enablement sessions, and build self-service tools.

Developing reusable AI capabilities: Create and maintain shared skills, plugins, and knowledge resources covering company processes, standards, security requirements, and brand guidelines.

Driving process automation: Proactively identify repetitive manual workflows that could benefit from AI, implement automation solutions, measure their impact, and continuously improve them.

Taking end-to-end ownership: Write technical specifications, plan and deliver projects independently, communicate progress asynchronously, and remain accountable for monitoring and improving solutions after deployment.

At least 5 years of experience as a software, platform, or infrastructure engineer, with a solid understanding of system design, data modeling, testing, debugging, and code review.

Between 6 and 12 months of experience shipping LLM-based systems to production and supporting agentic workflows used by real end users.

Daily, hands-on experience using AI coding agents such as Claude Code and Codex to write, test, and review production code.

Experience developing autonomous or multi-step agents with tool integrations, using technologies such as the Claude Agent SDK, Anthropic APIs, OpenAI APIs, or similar frameworks.

Practical experience building MCP servers, including OAuth authentication flows, scoped tokens, and user-level access controls.

Knowledge of identity and access management systems such as Okta or similar identity providers, including SSO protocols (SAML/OIDC), SCIM provisioning, group-based access, and device policies.

Experience creating automated system identities, including GitHub Apps, service accounts, API keys, and managing secrets through tools such as 1Password.

Understanding of security guardrails and governance practices, including least-privilege access, audit logging, CI checks, approval gates, allowlists, and policy-as-code.

Experience designing evaluation systems for LLM applications, including test sets, evaluation rubrics, CI regression testing, release gates, and live production scoring.

Practical experience implementing production observability through tracing, metrics, alerting, and dashboards, using tools such as OpenTelemetry, Grafana, Datadog, Langfuse, LangSmith, or Braintrust.

Experience monitoring and controlling AI expenditure across providers through API key limits, cost centers, budgets, and actionable alerts.

Strong programming skills in at least one of TypeScript, Python, or Go, alongside working knowledge of CI/CD pipelines and cloud infrastructure.

A demonstrated ability to independently identify and automate manual processes, supported by two or three concrete examples of previous work.

Strong written communication skills, with the ability to produce clear technical specifications, runbooks, and RFCs.

Ability to understand stakeholder needs, translate ambiguous requests into actionable technical requirements, and explain complex concepts to both executives and subject matter experts.

A proactive and autonomous working style, with the ability to prioritize effectively, challenge unsafe or low-value requests constructively, and communicate progress across time zones.

A strong sense of ownership, including the ability to validate solutions with users, monitor performance after deployment, and continuously iterate.

Experience in cryptocurrency, blockchain, payments, or fintech, particularly within regulated or audit-focused environments.

Experience creating reusable agent skills, plugins, or shared prompt libraries for organizational use.

Familiarity with model gateways such as OpenRouter, LiteLLM, or Portkey, including key management, model routing, and fallback strategies.

Experience extending open-source coding agents such as OpenCode or Pi.

Awareness of emerging agent identity standards, including MCP enterprise-managed authentication and Okta Cross App Access.

Knowledge of context and retrieval engineering, including vector stores, agent memory, and permission-aware retrieval systems.

Background in machine learning engineering, including model evaluation, fine-tuning, and model routing.

Experience working with agent runtimes and sandbox environments such as Modal, n8n, or containerized infrastructure.

Public technical contributions, such as open-source tools, servers, technical articles, or presentations demonstrating experience with production AI agents.

Remote-first work environment: Join a globally distributed workforce and work remotely.

Health insurance: Access medical, dental, and vision coverage, with employee and dependent coverage fully provided in certain countries, depending on the applicable plan.

Retirement benefits: Benefit from a company-matched 401(k) contribution of up to 3% for eligible employees in the United States.

Home office allowance: Receive a one-time allowance of $1,500 to set up your home workspace.

Professional development: Access an annual book allowance of $200 to support continuous learning.

Connectivity reimbursement: Receive $75 per month toward internet or phone expenses.

Flexible time off: Benefit from flexible paid time off arrangements.

Work equipment: Receive a company-issued laptop.

Well-being support: Access egg-freezing benefits, mental health resources, and employee wellness programs.

Inclusive workplace: Work in an environment committed to equal opportunity, accessibility, and providing reasonable accommodations throughout the recruitment process.