Applied AI Engineer, Zurich

AP Executive - Global Executive Search Agency; Zürich; Vor Ort; CHF 80.000 - 120.000; Vollzeit

Zusammenfassung

AP Executive - Global Executive Search Agency is seeking an experienced AI Engineer in Zürich, Switzerland. In this role, you'll take AI capability and turn it into practical solutions for end users, managing everything from model behavior to product implementation.

Ideal candidates have a strong background in machine learning, practical experience with Python and neural networks, and a mindset geared towards continuous improvement and real-world application.

Qualifikationen

• Solid grounding in machine learning and current neural network approaches.
• Direct experience training, fine-tuning, or deploying models.
• Ability to write clean code that holds up in production.

Aufgaben

• Take AI-driven features from concept through to a shipped product.
• Design and refine the tools and workflows driving agent behavior.
• Work closely with product and engineering colleagues.

Kenntnisse

Machine learning

Neural networks

Python

PyTorch

Model deployment

Tools

Vector databases

JAX

Jobbeschreibung

About the Business

Our client is a growing technology business developing software that applies modern AI models to everyday tasks. The engineering work is demanding: outputs need to hold up over longer interactions, stay grounded in context, and keep working reliably even though the underlying models don't always behave predictably. It's a small, senior team by design, with fast, shared decision-making and a strong emphasis on getting things right. The aim is a product that feels noticeably better than what people are used to today.

The Role

In this role, you'll take AI capability and turn it into something that actually works for end users. You'll take ownership of problems from start to finish – shaping how the model behaves, building what sits around it, and making sure it holds up once it's live. It's a role that spans machine learning, engineering, and product, with one underlying aim: getting AI to perform well in everyday use, not only in controlled demonstrations.

What You'll Be Doing

• Taking AI-driven features from concept through to a shipped, working product
• Designing and refining the prompts, tools, memory, and workflows that drive agent behaviour
• Converting raw model output into something structured, dependable, and predictable
• Tracing and fixing problems anywhere in the stack - model, orchestration layer, infrastructure, or interface
• Tuning for speed, cost, and dependable performance in live usePutting together simple, practical evaluation methods that reflect real usage
• Working closely with product and engineering colleagues to turn loosely defined problems into working solutions

Signs of Success

• Live models consistently hit the accuracy, speed, and reliability bar expected of them
• Problems in production get spotted early, diagnosed properly, and fixed at the source
• The pipelines, training routines, and inference systems behind the product stay dependable and easy to maintain
• Strong working relationships across engineering, product, and research help features land reliably
• Changes to the models and systems are grounded in real usage data and lead to measurable gains

Tools & Technology

• Python
• PyTorch / JAX
• Large language models, both commercial APIs and open‑weight options
• Model serving and inference infrastructure
• Vector databases

What We're Looking For

• A solid grounding in machine learning and current neural network approaches
• Direct experience training, fine‑tuning, or deploying models
• The ability to write clean code that holds up in production
• Ease moving between layers - model, infrastructure, and product
• Sound judgement when working through unclear or fast‑changing problems
• A natural preference for shipping, testing, and refining over time