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Cast AI is an automation platform that optimizes cloud-native and AI infrastructure at scale through autonomous decision-making embedded in Kubernetes and cloud environments. The company has achieved unicorn status (>$1B valuation as of January 2026) and serves over 2,100 customers including Akamai, BMW, Cisco, FICO, HuggingFace, and others.
You will be the founding customer-facing engineer for Kimchi, Cast AI's flagship product within the Customer Success organization. Kimchi executes multi-model LLM workflows and autonomous agents with embedded governance and cost control, intelligently routing across frontier and open-source models while enabling teams to establish budget caps, enforce model access policies, configure roles, and audit total cost of ownership through deep analytical tracking. Through Kimchi Studio, developers gain desktop access to agentic workflows to accelerate adoption without sacrificing operational governance or spend visibility.
This is a hands-on individual contributor role at the intersection of inference optimization, autonomous coding agents, multi-model routing, and LLM governance. You will architect and execute the growth strategy that scales Kimchi adoption and creates revenue expansion opportunities for existing customers.
Key responsibilities include:
- Lead hands-on demos of Kimchi Coding, Studio, and Teleport products
- Set up Kimchi Studio workspaces within customer environments, configuring worktrees, integrations (Slack, repos, docs), and skills for agents
- Design and drive implementation of governance policies: spending limits per team/developer, cost attribution by project, model routing preferences
- Help teams integrate Kimchi's serverless inference API and optimize model selection for their workloads
- Translate customer feedback into product input, identifying where agents fail, UX needs improvement, or governance policies don't align with customer needs
The role offers a flexible, remote-first global environment with a team of cloud experts. Benefits include equity options, fast-paced workflow (most features completed in 1-4 weeks), 10% time for personal projects, learning budget for conferences and courses, team-building budget, equipment budget, and extra days off.
REQUIREMENTS:
- Deep hands-on experience with LLMs: deployed, fine-tuned, evaluated, or debugged them; understand why model A outperforms model B for specific tasks
- Experience operating in fast-moving, change-prone environments
- Built or optimized inference systems; understand latency, throughput, and total cost of ownership; can ballpark costs and performance implications
- Ability to explain complex inference concepts to non-ML engineers and demonstrate business impact
- Commercial thinking: understand revenue expansion, identify expansion opportunities within customer organizations, design strategies to capture them; combine technical excellence with entrepreneurial growth mindset
