Applied AI Engineer

We are seeking an
* Applied AI Engineer
* to drive the transformation of cutting‑edge AI capabilities into reliable, real‑world product experiences. This role is central to building a proactive AI smart assistant that seamlessly integrates into users’ daily lives—handling complex, multi‑step workflows with consistency, context awareness, and dependable task completion. You will bridge the gap between advanced machine learning research and scalable, production‑grade systems, ensuring AI features perform reliably under real‑world conditions.

Your work will directly shape how users interact with AI, turning abstract model potential into tangible, trustworthy functionality across conversations, errands, and organization. This is a hands‑on, end‑to‑end role focused on building robust, measurable, and user‑validated AI systems in a fast‑paced, product‑driven environment.

• Design, implement, and ship AI‑powered features from concept to production, spanning model behaviour, orchestration, and user experience.
• Develop and refine agent workflows, including prompt engineering, tool integration, memory management, and state persistence.
• Translate unpredictable model outputs into structured, reliable, and consistent system behaviours.
• Diagnose and resolve issues across the full stack—model performance, inference latency, system reliability, and user‑facing logic.
• Optimize AI systems for cost, latency, scalability, and long‑term maintainability in production.
• Build lightweight evaluation frameworks to monitor real‑world AI performance and guide iterative improvements.
• Collaborate with product, engineering, and research teams to define problems, prototype solutions, and deliver measurable outcomes.
• Iterate rapidly based on user feedback, failure analysis, and performance metrics to improve reliability and utility.

• Strong foundation in machine learning, deep learning, and modern neural network architectures.
• Hands‑on experience with training, fine‑tuning, or deploying machine learning models in production.
• Proven experience working with large language models (LLMs), agent systems, tool use, memory systems, or AI‑powered product features.
• Proficiency in writing clean, efficient, and production‑ready code in Python.
• Experience across multiple layers of the stack: model development, inference serving, backend systems, and product integration.
• Demonstrated ability to solve ambiguous, open‑ended problems in fast‑moving environments.
• Strong bias toward shipping, continuous iteration, and measurable impact.
• Practical mindset focused on reliability, usability, and real‑world performance of AI systems.
• Python
• PyTorch / JAX
• LLMs (OpenAI‑style APIs, LLaMA, Qwen, and similar)
• Inference / serving (e.g., vLLM)
• Model evaluation and monitoring tools
• AI orchestration, memory, and tool‑use frameworks