<h3>Company</h3><p style="min-height:1.5em">A1 is building a proactive AI smart assistant for everyday users to bring intelligence to conversations, errands, organising and workflows.</p><p style="min-height:1.5em">Our product focuses on achieving high reliability for long-running workflows, persistent context, and real-world task completion. The system must handle multi-step reasoning, interact with external tools, and remain reliable despite non-deterministic model behavior.</p><h3>Role</h3><p style="min-height:1.5em">As an Applied AI Engineer, you will turn model capabilities into real product behavior. You will own problems end-to-end, from shaping model behavior, to building the systems around it, to ensuring it performs reliably in production.</p><p style="min-height:1.5em">This role sits at the intersection of machine learning, systems, and product, focusing on making AI actually work for users, not just in demos, but in real-world usage.</p><h3><strong>Focus</strong></h3><ul style="min-height:1.5em"><li><p style="min-height:1.5em">Build and ship AI features end-to-end (model → system → user experience)</p></li><li><p style="min-height:1.5em">Design and iterate on prompts, tools, memory, and agent workflows</p></li><li><p style="min-height:1.5em">Turn raw model outputs into structured, reliable, and predictable behaviors</p></li><li><p style="min-height:1.5em">Debug issues across the full stack (model, orchestration, infra, UX)</p></li><li><p style="min-height:1.5em">Optimize for latency, cost, and production reliability</p></li><li><p style="min-height:1.5em">Develop lightweight evaluation frameworks to measure real-world performance</p></li><li><p style="min-height:1.5em">Work closely with product and engineering to translate ambiguous problems into working systems</p></li></ul><h3><strong>Tech Stack</strong></h3><ul style="min-height:1.5em"><li><h3>Python</h3></li><li><h3>PyTorch / JAX</h3></li><li><p style="min-height:1.5em">LLMs (OpenAI-style APIs, LLaMA, Qwen, etc.)</p></li><li><h3>Inference / serving (e.g. vLLM)</h3></li><li><h3>Vector DB</h3></li></ul><h3><strong>Ideal Experience</strong></h3><ul style="min-height:1.5em"><li><p style="min-height:1.5em">Strong foundation in machine learning and modern neural network architectures.</p></li><li><p style="min-height:1.5em">Hands-on experience with training, fine-tuning, or deploying ML models</p></li><li><p style="min-height:1.5em">Ability to write clean, production-quality code</p></li><li><p style="min-height:1.5em">Comfort working across abstraction layers (model → infra → product)</p></li><li><p style="min-height:1.5em">Strong problem-solving skills in ambiguous, fast-moving environments</p></li><li><p style="min-height:1.5em">Bias toward shipping, iteration, and continuous improvement</p></li></ul><h3><strong>Outcomes</strong></h3><ul style="min-height:1.5em"><li><p style="min-height:1.5em">ML models in production meet expected accuracy, latency, and reliability targets.</p></li><