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 use
Putting 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
To apply or discuss this role further, please contact Don Fletcher via don.fletcher@ap-executive.conm.
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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 use
Putting 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
To apply or discuss this role further, please contact Don Fletcher via don.fletcher@ap-executive.conm.
#J-18808-Ljbffr
Applied AI Engineer, Zurich Arbeitgeber: jobs.jerseyeveningpost.com-job boards
Unser Unternehmen ist ein hervorragender Arbeitgeber, der eine dynamische und innovative Arbeitsumgebung in Zürich bietet. Wir fördern eine Kultur der Zusammenarbeit und des kontinuierlichen Lernens, in der Mitarbeiter die Möglichkeit haben, ihre Fähigkeiten in der Anwendung von KI-Technologien weiterzuentwickeln. Mit einem kleinen, erfahrenen Team und einer starken Ausrichtung auf qualitativ hochwertige Ergebnisse bieten wir nicht nur spannende Herausforderungen, sondern auch die Chance, an bedeutenden Projekten zu arbeiten, die den Alltag der Nutzer verbessern.
Kontaktdaten:
jobs.jerseyeveningpost.com-job boards Recruiting-Team
