Applied AI Engineer

About the role

ActAI builds proactive applications for people who are not used to complex prompting, starting with an agent-first mobile communication app. As an Applied AI Engineer you turn model capabilities into real product behavior, owning problems end-to-end from shaping model behavior to building the systems around it and ensuring reliable production performance. The role sits at the intersection of machine learning, systems and product.

Key responsibilities

• Build and ship AI features end-to-end, from model to system to user experience
• Design and iterate on prompts, tools, memory and agent workflows
• Turn raw model outputs into structured, reliable and predictable behaviors
• Debug issues across the full stack (model, orchestration, infrastructure, UX)
• Optimize for latency, cost and production reliability
• Develop lightweight evaluation frameworks to measure real-world performance
• Work with product and engineering to turn ambiguous problems into working systems

Required profile

• Strong foundation in machine learning and modern neural network architectures
• Hands-on experience training, fine-tuning or deploying ML models
• Ability to write clean, production-quality code
• Comfort working across abstraction layers (model, infrastructure, product)
• Strong problem-solving skills in ambiguous, fast-moving environments
• Bias toward shipping, iteration and continuous improvement

Required skills

• Python
• PyTorch / JAX
• Large language models (OpenAI-style APIs, LLaMA, Qwen, etc.)
• Inference and serving (e.g. vLLM)
• Vector databases

The interview process involves 3 to 4 virtual or onsite interviews, evaluated by the technical team, with a prompt decision.