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CAST AI
14 days ago
Grade
Senior
Experience
from 5 years
Work format
Remote
Employment
Full-time
English level
B2, upper-intermediate
An email or a Telegram message to the recruiter
Great models are raw ingredients. Kimchi is what happens when you ferment them with good engineering. It started inside CAST AI helping companies run LLMs on their own Kubernetes clusters; today it runs its own coding harness on any model you pick, and the router deciding which model answers each request is built in-house too.
The promise is one enforcement layer for your AI: budgets, spend attribution, and model routing, applied before a request ever hits a model. The Auto Router decides, for every request, which model serves it. It works with eight open-source models whose input prices span more than 20×, cached input up to 50× cheaper than fresh, frontier providers on customers' own keys, and a coding agent that fires hundreds of requests before its task is done. Frontier-level task success at a fraction of the token cost is the product, and it stays true only if the model inside the router keeps getting better. That model is yours.
Every request gets a decision: which model serves it. This is an applied-ML seat, not an infrastructure one. Platform engineers own the proxy, the streaming path, and the fleet. You own the model inside them: the training data, the objective, the evaluation, and the iteration loop that ships a better router every cycle.
Routing is a prediction problem wearing a product's clothes. For every request you predict the quality each candidate model would deliver, what it would cost, and how fast it would decode, and you have to be right enough, in aggregate, that the savings are real and the quality never dips below frontier.
The data is messy: rollout outcomes from benchmark harnesses, per-request records from the live fleet, override signals, and token streams whose speed depends on the serving concurrency at the moment. Speed itself is a correctness variable: on deadline-bound agentic work, a model that decodes three times slower doesn't do the same work slower, it does less work. That's why naive cost-per-token objectives quietly optimize for the wrong thing. A router change ships only after production-parity evaluation with error bars, then gets held to its numbers in live traffic.
Stack: Python; PyTorch; SQL at scale (ClickHouse); OpenAI- and Anthropic-compatible LLM APIs; benchmark and rollout harnesses (RouterBench-class); GitLab CI.
Great models are raw ingredients. Kimchi is what happens when you ferment them with good engineering. It started inside CAST AI helping companies run LLMs on their own Kubernetes clusters; today it runs its own coding harness on any model you pick, and the router deciding which model answers each request is built in-house too.
The promise is one enforcement layer for your AI: budgets, spend attribution, and model routing, applied before a request ever hits a model. The Auto Router decides, for every request, which model serves it. It works with eight open-source models whose input prices span more than 20×, cached input up to 50× cheaper than fresh, frontier providers on customers' own keys, and a coding agent that fires hundreds of requests before its task is done. Frontier-level task success at a fraction of the token cost is the product, and it stays true only if the model inside the router keeps getting better. That model is yours.
Every request gets a decision: which model serves it. This is an applied-ML seat, not an infrastructure one. Platform engineers own the proxy, the streaming path, and the fleet. You own the model inside them: the training data, the objective, the evaluation, and the iteration loop that ships a better router every cycle.
Routing is a prediction problem wearing a product's clothes. For every request you predict the quality each candidate model would deliver, what it would cost, and how fast it would decode, and you have to be right enough, in aggregate, that the savings are real and the quality never dips below frontier.
The data is messy: rollout outcomes from benchmark harnesses, per-request records from the live fleet, override signals, and token streams whose speed depends on the serving concurrency at the moment. Speed itself is a correctness variable: on deadline-bound agentic work, a model that decodes three times slower doesn't do the same work slower, it does less work. That's why naive cost-per-token objectives quietly optimize for the wrong thing. A router change ships only after production-parity evaluation with error bars, then gets held to its numbers in live traffic.
Stack: Python; PyTorch; SQL at scale (ClickHouse); OpenAI- and Anthropic-compatible LLM APIs; benchmark and rollout harnesses (RouterBench-class); GitLab CI.
A background check may be conducted at the final stage of recruitment through a third-party provider, Checkr. Cast AI does not provide any form of visa sponsorship/work permit.
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