Data and AnalyticsDeepTechInformation TechnologySoftware
-
Vultron
🇺🇸US
Data and AnalyticsDeepTechInformation TechnologySoftware
$22M
Replit
🇺🇸US
Software
$872M
Who you are
4+ years of professional software engineering experience
12–18 months building and shipping LLM‑powered products (from prototype to GA) with meaningful customer adoption. Please include links, demos, or a brief write‑up if possible
Strong Python and TypeScript experience. Solid systems design and API fundamentals
Hands‑on with LLM APIs, embeddings, vector databases, retrieval patterns and agent/tool design
Pragmatic prompt engineering and prompt/version control
Production chops: cloud (GCP), containers, CI/CD, monitoring/alerting; comfort owning services in prod
Product sense, clear communication, and a bias to ship
E‑commerce, recommendations, ranking or ads experience
Experience with streaming responses, Streamable HTTP/SSE/WebSocket patterns for real-time AI interactions
Real‑time personalization and event pipelines (e.g., Kafka, pub/sub)
Front‑end (React) skills for rapid prototyping of AI UX
Experimentation/analytics tools (e.g., PostHog or similar)
What the job involves
We’re searching for a product‑minded Software Engineer to help build out Pierre, FERMÀT’s AI Agent, into a production‑grade intelligence engine that powers personalization, automation, and operational insights across our commerce platform
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You will design and build LLM‑powered features end‑to‑end: orchestration (tools/agents/function‑calling), retrieval, evaluations and productionization (APIs, observability, cost/latency). If you love turning ambiguous problems into shipped experiences—and you have experience taking AI products to market in the last 12 to 18 months, this role is for you
Take AI products to market: prototype quickly, instrument, A/B test, and iterate based on quality, conversion impact, latency, and cost
Own LLM orchestration: multi-agent systems, tool use, function‑calling, structured outputs, routing, context windows, retrieval and evals
Build evaluation & observability: golden datasets, automated evals, prompt/version management and monitoring
Productionize reliably: scalable APIs/services, workers/queues, caching and provider failover. Optimize performance while keeping costs reasonable
Data foundations: integrate first‑party events and catalog data; respect data contracts
Vendor strategy: evaluate and integrate multiple model providers (e.g., OpenAI, Anthropic, Google) while tracking quality, cost, and reliability
Raise the bar: document best practices, lead design reviews, and mentor engineers on AI patterns
