Senior Applied AI Solutions Engineer

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.

How Jobgether works:
We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.
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