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You will own the end-to-end development of production AI systems, specifically focusing on voice and email agents for freight and logistics. This includes scoping customer problems, implementing features, deploying systems, and monitoring production performance.
Join a small, early-stage team building production AI systems for freight and logistics workflows. You will own work end to end, from understanding customer needs through implementation, deployment, and production monitoring, with a focus on voice and email agents.
Scope customer problems, build features across the stack, deploy them, and monitor production behavior.
Develop voice and email agents, including speech pipelines, LLM orchestration, and deterministic safeguards for prices, terms, and actions.
Create evaluation systems using golden datasets, offline and online checks, regression and hallucination testing, and tool-call correctness checks.
Build embedding-based retrieval and matching with reranking, balancing quality, latency, and cost.
Instrument production systems and use observability and rollout strategies to improve reliability.
At least 5 years of professional software engineering experience shipping production software with end-to-end, full-stack ownership.
Hands-on experience taking LLM features to production, including architecture, tool-calling, retrieval, and robust evaluation.
Production experience with React, Next.js, Supabase, and Python.
Experience with agent or voice tooling such as Pipecat, LangChain, LiveKit, or equivalent technologies.
Strong system design fundamentals, including streaming, concurrency, SQL, and reasoning about scale and cost.
Experience with production observability and deployment rollouts, and daily use of generative AI coding tools with careful verification.
Voice or real-time systems experience, early-stage engineering experience, or experience building complex tool-use systems is valuable.
Compensation is $5,000 to $9,000 USD monthly. Visa sponsorship is not available.
Fully remote, based in Latin America.
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