Applied AI Engineer

About the role

As an Applied AI Engineer you will turn model capabilities into real product behavior, owning problems end‑to‑end from shaping model behavior to building the surrounding systems and ensuring reliable production performance.

Key responsibilities

• Build and ship AI features end‑to‑end (model → system → user experience).
• Design and iterate on prompts, tools, memory, and agent workflows.
• Transform 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.
• Collaborate closely with product and engineering teams to translate ambiguous problems into working systems.

Required profile

• Strong foundation in machine learning and modern neural network architectures.
• Hands‑on experience with training, fine‑tuning, or deploying ML models.
• Ability to write clean, production‑quality code.
• Comfort working across abstraction layers (model → infra → 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/serving frameworks (e.g., vLLM)
• Vector databases