Senior Applied AI Solutions Engineer

Jobgether; España; Presencial; EUR 174.000 - 304.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Competitive USD salary

Autonomy and ownership

Global, collaborative team

Career growth opportunities

Descripción de la vacante

Jobgether is seeking a Senior Applied AI Solutions Engineer based in Spain, operating at the intersection of ML engineering, customer success, and product development. You will help enterprise customers move complex ML workloads from proof of concept into production, with half your time on direct customer engagements.

You will build prototypes, explore AI techniques, and translate discoveries into practical product feedback, influencing roadmaps across the platform.

Formación

• Strong hands-on experience with modern ML systems, including fine-tuning large models and production-ready pipelines.
• Fluency with ML stack: PyTorch, Hugging Face, CUDA fundamentals, Kubernetes for ML, MLflow or equivalents, vector databases.
• Experience working directly with enterprise ML teams in solutions/customer engineering roles.
• Ability to read papers and translate findings into working implementations.
• Strong understanding of ML infra and troubleshooting complex workloads.
• Excellent communication, able to explain complex concepts to diverse audiences.
• Customer-oriented with engineering rigor and initiative.
• Ability to switch between research, prototyping, and product feedback.

Responsabilidades

• Build polished prototypes and demos across serverless inference, MLops, and applied AI use cases.
• Support enterprise customers through PoC design, onboarding, validation, and production handoff.
• Bridge between customer ML teams and the platform to accelerate time-to-value in early adoption.
• Research emerging AI techniques and turn discoveries into working prototypes.
• Translate research into technical write-ups and actionable product feedback.
• Identify recurring issues and provide input to the product roadmap.
• Develop reusable assets like notebooks, reference architectures, and benchmarks.
• Collaborate with sales, product, and engineering to convert insights into solutions.
• Track AI ecosystem trends to inform the team over 6–12 months.

Conocimientos

ML systems

PyTorch/HF

Enterprise ML teams

Research techniques

ML infrastructure

Communication

Customer orientation

Adaptability

Applied AI domains

MLOps tools (Kubeflow/Metaflow/Argo/Ra

Cloud provider experience

Open-source contributions

Herramientas

Kubeflow

Metaflow

Argo

Ray

CUDA

Descripción del empleo

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 Spain.

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.