Applied AI Engineer

European Recruitment BV; United States; On-site; USD 120,000 - 170,000; Full time

Job summary

European Recruitment BV is seeking an Applied AI Engineer to turn model capabilities into real product behavior. You will own problems end‑to‑end—from shaping model behavior to building the surrounding systems and ensuring reliable production performance.

This role sits at the intersection of machine learning, systems, and product, focusing on making AI work for users beyond demos and into real-world usage.

Qualifications

• Strong foundation in machine learning and modern neural networks.
• 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.

Responsibilities

• Build and ship AI features end-to-end (model → system → 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, infra, UX).
• Collaborate with product and engineering to translate ambiguous problems into working systems.

Skills

Machine Learning

Production code

Problem solving

Ambiguity management

Tools

Python

PyTorch

JAX

LLMs

vLLM

Vector DB

Job description

Applied AI Engineer

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.

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.

Focus

• Build and ship AI features end-to-end (model ? system ? 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, infra, UX)
• Optimize for latency, cost, and production reliability
• Develop lightweight evaluation frameworks to measure real-world performance
• Work closely with product and engineering to translate ambiguous problems into working systems

Tech Stack

• Python
• PyTorch / JAX
• LLMs (OpenAI-style APIs, LLaMA, Qwen, etc.)
• Inference / serving (e.g. vLLM)
• Vector DB

Ideal Experience

• 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

Outcomes

• ML models in production meet expected accuracy, latency, and reliability targets.
• Production issues are identified quickly, debugged effectively, and root causes addressed.
• Data pipelines, training loops, and inference systems are robust, reproducible, and maintainable.
• Collaborates effectively with engineers, product, and research teams to deliver reliable ML‑powered features.
• Iterations on models and systems are driven by real‑world signals and measurable improvements.