Senior Applied AI Solutions Engineer

Jobgether; Lavamünd; Vor Ort; EUR 174.000 - 304.000; Vollzeit

Benefits dieser Stelle

Competitive base salary

Comprehensive benefits

Flexible work arrangements

Professional growth

Impactful AI projects

Global team

Zusammenfassung

Jobgether is seeking a Senior Applied AI Solutions Engineer based in Switzerland. The role blends advanced ML engineering, customer success, and product development to move enterprise workloads from proof of concept into production.

You will spend about half of your time with customers, solving technical challenges and accelerating platform adoption, while the rest builds prototypes and explores new AI techniques across the product portfolio.

Qualifikationen

• Hands-on ML systems experience including fine-tuning models and distributed training.
• Experience with PyTorch, Hugging Face, CUDA, Kubernetes for ML, MLflow, and vector databases.
• Experience with enterprise ML teams in a solutions/customer engineering capacity.
• Ability to translate research into working implementations.
• Strong ML infrastructure understanding and debugging across the stack.
• Excellent communication for technical and business audiences.
• Customer orientation with engineering rigor.
• Ability to handle varied environment: customer problems, rapid prototyping, product feedback.
• Experience in Physical AI, robotics, healthcare, or enterprise AI applications is a plus.
• Familiarity with Kubeflow, Metaflow, Argo, or Ray is a plus.
• Experience at a cloud provider or AI infrastructure company is beneficial.
• Publicly shared technical work is a plus.

Aufgaben

• Build polished prototypes and demos across serverless inference, MLflow, MLOps, and applied AI use cases, including Physical AI and healthcare.
• Support enterprise customers hands-on through proof-of-concept design, onboarding, validation, and transition to production.
• Act as a technical bridge between customer ML teams and the platform, diagnosing challenges and accelerating adoption.
• Research emerging applied AI techniques and turn discoveries into working prototypes.
• Translate research and customer experience into technical write-ups, recommendations, and product feedback.
• Identify recurring issues across deployments and inform the product roadmap with evidence.
• Develop reusable assets like notebooks, reference architectures, benchmarks, and guides.
• Collaborate with sales, product, engineering to convert insights into practical solutions.
• Track AI ecosystem developments to anticipate trends over the next 6–12 months.

Kenntnisse

Fine-tuning large models

Distributed training debugging

Production-grade ML pipelines

GPU-based inference optimization

PyTorch

Hugging Face

CUDA fundamentals

Kubernetes for ML

MLflow

Vector databases

Enterprise ML collaboration

Tools

Kubeflow

Metaflow

Argo

Ray

MLflow

Jobbeschreibung

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Applied AI Solutions Engineer based in Switzerland.

This role sits at the intersection of advanced ML engineering, customer success, and product development within a rapidly evolving AI infrastructure environment.

You will help enterprise customers move complex machine learning workloads from proof of concept into production.

Around half of your time will be spent working directly with customers, solving technical challenges and accelerating their adoption of the platform.

The remainder will focus on building prototypes, exploring emerging AI techniques, and demonstrating new possibilities across the product portfolio.

Your hands-on field experience will directly influence product priorities, helping transform recurring customer challenges into concrete improvements.

You will work across areas including inference, databases, MLOps, serverless AI, agentic architectures, and specialized AI applications.

This is a highly autonomous role for an engineer who wants technical depth, customer impact, and a direct voice in the evolution of AI products.

Accountabilities

• Build polished prototypes and technical demonstrations across serverless inference, databases, MLflow, MLOps, and applied AI use cases, including Physical AI and healthcare and life sciences.
• Support enterprise customers hands-on through proof-of-concept design, technical onboarding, validation, and the transition from experimentation to production.
• Act as a technical bridge between customer ML teams and the platform, helping diagnose challenges and accelerate time-to-value during the first months of adoption.
• Research emerging applied AI techniques, including new training approaches, inference optimizations, agentic architectures, and frameworks, and turn relevant discoveries into working prototypes.
• Translate research and customer experience into technical write-ups, recommendations, and actionable product feedback.
• Identify recurring issues across customer deployments and provide specific, evidence-based input that can shape the product roadmap.
• Develop reusable technical assets such as notebooks, reference architectures, benchmarks, and implementation guides to reduce onboarding friction.
• Collaborate with sales, product, engineering, and customer teams to ensure technical insights are converted into practical solutions and product improvements.
• Track developments across the AI ecosystem and help the wider team anticipate important applied AI trends over the next 6–12 months.

Requirements

• Strong hands-on experience with modern machine learning systems, including fine-tuning large models, debugging distributed training workloads, building production RAG or agentic pipelines, and optimizing GPU-based inference.
• Fluency across the modern ML technology stack, including PyTorch, Hugging Face, CUDA fundamentals, Kubernetes for ML, MLflow or equivalent platforms, and vector databases.
• Experience working directly with enterprise ML teams, whether in a solutions engineering, customer engineering, ML engineering, or closely related capacity.
• Demonstrated ability to research emerging techniques, read technical and academic papers, and translate relevant findings into working implementations.
• Strong understanding of ML infrastructure and the ability to troubleshoot complex workloads across the stack.
• Excellent communication skills, with the ability to adapt technical explanations for audiences ranging from ML engineers to senior technology and business leaders.
• Strong customer orientation combined with the ability to maintain technical depth and engineering rigor.
• Ability to work effectively in a highly varied environment, moving between customer problems, technical research, rapid prototyping, and product feedback.
• Experience in Physical AI, robotics, simulation, healthcare and life sciences, drug discovery, medical imaging, clinical NLP, or enterprise AI application development is an advantage.
• Familiarity with large-scale MLOps technologies such as Kubeflow, Metaflow, Argo, or Ray is a plus.
• Previous experience at a cloud provider or AI infrastructure company is beneficial.
• Publicly shared technical work, such as useful notebooks, talks, technical articles, or open-source contributions, is a strong plus.

Benefits

• Competitive compensation, with the advertised base compensation range of $200,000–$350,000 USD, depending on experience, skills, qualifications, level, and location.
• Comprehensive benefits package.
• Flexible working arrangements with significant ownership and autonomy.
• Professional growth, continuous learning, and career development opportunities.
• Opportunity to work on impactful AI and ML infrastructure projects.
• Collaborative, innovative, and fast-moving working environment.
• International environment with talented teams distributed across multiple locations.
• Opportunity to work directly with enterprise customers and influence product direction.
• Exposure to emerging areas of applied AI, including generative AI, agentic systems, MLOps, inference optimization, Physical AI, and healthcare applications.
• An environment that values initiative, technical excellence, trust, and meaningful ownership.
• Inclusive workplace committed to equal employment opportunities and a diverse, supportive culture.