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Spherecast
LLM Engineer (Visa Sponsorship)
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Based on New York data
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What they do
A Hunter or Trapper catches and kills mammals, birds or reptiles mainly for meat, skin, feathers and other products for sale or delivery on a regular basis to wholesale buyers, marketing organizations or at markets.
$41,622 / year median in New York
-8% projected decline
Job Description
LLM Engineer - Agnes (Visa Sponsorship) As a LLM Engineer at Spherecast , you will be responsible for building Agnes from the ground up - our AI Supply Chain Manager that decides what to produce, where to make it, and how to move it through factories, warehouses, and channels. This is a fast-paced, highly autonomous role for someone who can own AI systems end-to-end : from prototypes to production, from prompts to tested and evaluated pipelines, from agents to real-world outcomes (POs, TOs, bookings). You'll work directly with the core team to turn the physical flow of goods into something as programmable as code. If you're a builder who thrives at the intersection of LLMs, agents, systems engineering, and messy real-world data , this is your opportunity to shape how global brands run their supply chains. What we're looking for You are a great fit if you have: Hands-on experience with modern LLM APIs (e.g. Anthropic, OpenAI, DeepSeek, OpenRouter, Gemini, Moonshot) and have shipped features using them. Strong intuition for large language model selection - you understand the strengths, weaknesses, latency/cost tradeoffs, and ideal use cases of different LLMs. Practical experience with the HuggingFace ecosystem in real projects. A track record of building LLM-powered automations or agents that are core to a production system, not just internal demos or playgrounds. Experience designing and maintaining evaluation pipelines to iterate quickly and safely on prompts and workflows. Strong prompting and system-design skills - you know how to design tools/functions, structured outputs, and multi-step agent flows that are robust to edge cases. Experience with LLM observability and monitoring (logging, traces, quality metrics, feedback loops) to track and improve production accuracy over time.