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AI Engineer
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We're looking for a Lead AI Engineer to own and evolve our applied AI stack end-to-end. This is a senior, hands-on role for someone who enjoys building real LLM systems in production — from speech-to-text and model fine-tuning to agent orchestration and the infrastructure that keeps everything reliable at scale. This role combines deep technical execution with technical leadership and coaching.
What You'll Do
• LLM Systems & Agents: Design, build, and operate production LLM pipelines, including multi-step agents, tool use, retrieval, memory, and orchestration using frameworks like LangGraph (or equivalents).
• Speech & Multimodal AI: Own and evolve our STT pipelines (streaming and batch), model selection, evaluation, and integration with downstream LLM workflows.
• Model Adaptation & Fine-Tuning: Lead fine-tuning, distillation, and evaluation of models (LLMs, STT, embeddings). Define when to fine-tune vs. prompt vs. retrieve.
• Workflow Orchestration: Build robust, long-running, and fault-tolerant AI workflows using systems like Temporal (or similar), including retries, compensation, and observability.
• AI Infrastructure: Design the infrastructure that supports AI workloads — inference services, evaluation pipelines, data flows, and cost/performance optimization.
• Reliability & Evaluation: Define quality bars, offline and online evaluation strategies, guardrails, and monitoring for AI systems in production.
• Developer Experience: Build internal tooling, abstractions, and standards that make it easy for other engineers to build on top of the AI platform.
• Technical Leadership: Coach and mentor engineers, review designs and code, and help set technical direction without becoming a full-time people manager.
What We're Looking For
• Proven experience building production LLM systems, not just demos or notebooks.
• Strong hands-on experience with LLMs, agents, RAG, and tool-calling architectures.
• Solid background in speech-to-text systems and multimodal pipelines.
• Experience with workflow orchestration for AI systems (Temporal or equivalent).
• Practical experience fine-tuning and evaluating models, including tradeoffs around cost, latency, and quality.
• Strong software engineering fundamentals: system design, APIs, testing, and observability.
• Comfort owning systems end-to-end — from model behavior to infrastructure and runtime reliability.
• Ability to balance experimentation with production rigor.
• Experience mentoring engineers and raising the technical bar of a team.
This role is ideal for someone who wants to stay deeply technical while shaping how AI systems are built, deployed, and scaled across the company.
