• Optimize model inference using advanced techniques including quantization (GPTQ, AWQ, GGUF), distillation, pruning, and speculative decoding • Build and integrate GenAI capabilities beyond LLMs, including computer vision, image generation (Stable Diffusion, FLUX), and multimodal models • Design and implement pre-processing and post-processing pipelines, including prompt engineering, structured output parsing, guardrails, and context management • Build RAG systems, embedding pipelines, and semantic retrieval architectures for enterprise AI applications • Drive model selection, benchmarking, and cost/performance trade-off decisions across AI services • Build evaluation frameworks to measure model quality, latency, reliability, and production performance • Build production AI systems that go beyond experimentation and notebooks, focusing on scalability, reliability, and maintainability • Collaborate closely with platform, infrastructure, and product teams to deliver integrated AI services • Contribute to AI platform architecture and long-term technical direction • Participate in the full lifecycle of AI systems, from research and prototyping to production deployment and operations

• You will build the systems that make Alaro’s product meaningfully useful to lawyers: agents, extraction tools, and controllers that automate repetitive work and reliably execute delegated tasks. • At a high level: we are building a Cursor-like experience for legal documents and legal research - where an agent can search, reason over sources, and propose or apply edits while the lawyer stays in control. • This is an engineering-first applied AI role. To succeed, you’ll need to understand the full product surface (editor, retrieval, dashboard workflows), so you can spot where an agent should be given control - and ship it end-to-end. • Document editing: an agent proposes changes and generates/applies patches to DOCX documents, built for legal reality: multiple collaborators, tracked changes, strict formatting, and high expectations for correctness. • Search & retrieval: when a lawyer asks “search legislation” or “find similar contracts in our database,” the agent decomposes that into targeted queries, retrieves the right sources, and grounds outputs with citations. • Deep research workflows: solving legal problems requires iterative investigation, where agents make progress step by step while keeping their active context focused and manageable. • Training models: fine-tuning models becomes a necessity to optimize the above workflows. As internal data matures, the agent can learn from past interactions to become more intelligent.

• Own the AI product roadmap across servicing, collections, and compliance, driving from vision through execution. • Translate operational workflows and lender pain points into clear AI feature requirements and measurable outcomes. • Lead cross-functional teams (engineering, data, operations, legal/compliance) to design, build, and ship AI features. • Evaluate and integrate with third-party AI providers; own partner selection, pricing, integration strategy, and ongoing performance monitoring. • Work closely with legal and compliance teams to ensure all AI solutions meet regulatory, audit, and explainability standards. • Instrument AI features for performance measurement (accuracy, deflection, compliance rates, etc.) and drive continuous iteration. • Act as the internal evangelist for AI capabilities, providing product expertise to operations, customer success, and clients. • Stay ahead of emerging AI capabilities and vendor landscape to inform the roadmap.

• Develop and maintain GenAI-powered application features and workflows. • Integrate large language model APIs and generative AI capabilities. • Optimize system performance and ensure robust user experience. • Work with cloud platforms to deploy scalable AI solutions. • Conduct thorough testing and maintain clear technical documentation.