Senior Sales Engineer - Token Factory

Job description

**This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Sales Engineer - Token Factory based in Netherlands.** This is a foundational technical role supporting customers building and scaling performance-sensitive AI inference workloads. You will bridge customer requirements, commercial objectives, and engineering realities from initial discovery through production validation. The role combines deep technical architecture with customer engagement, helping ensure that proposed solutions are scalable, efficient, and economically viable. You will work closely with Sales and Engineering to shape strategic opportunities and make informed technical decisions before significant resources are committed. You will also identify recurring workload patterns and translate customer needs into actionable insights for product and platform evolution. The environment is fast-moving, international, engineering-led, and focused on solving complex challenges at the forefront of AI infrastructure. Success means building customer trust while improving deal quality, PoC-to-production conversion, and the effective use of engineering capacity. ### Accountabilities - Lead in-depth technical discovery with engineering teams, technical founders, and other customer stakeholders to understand models, traffic expectations, latency requirements, GPU economics, and system dependencies. - Translate customer objectives into production-ready architectures while identifying technical risks, scalability constraints, and hidden dependencies early. - Partner closely with Sales on strategic opportunities, using architectural clarity to influence deal strategy and prevent technically misaligned commitments. - Define measurable PoC success criteria covering latency, time to first token (TTFT), throughput, cost, and other relevant performance indicators. - Assess workload complexity and determine the appropriate level of optimization, technical support, engineering involvement, and GPU capacity required. - Drive structured Go/No-Go decisions and help ensure PoCs remain appropriately scoped, economically justified, and free from uncontrolled customization or hidden R&D requirements. - Identify recurring customer configuration and workload patterns, quantify demand for advanced inference optimizations, and communicate structured insights to Product and Engineering. - Contribute to the evolution of platform capabilities by turning real-world workload data and customer requirements into actionable product opportunities. - Help ensure strategic deals are technically sound before engineering engagement, engineering resources are allocated predictably, and customers receive scalable solutions. ## Requirements - Deep understanding of AI inference systems and GPU-backed infrastructure, with strong knowledge of performance-sensitive environments. - Hands-on experience working with LLM workloads and the architectural trade-offs involved in deploying inference systems at scale. - Practical experience with inference frameworks and libraries such as vLLM, SGLang, or TensorRT-LLM. - Strong ability to reason about latency, throughput, cost, scalability, resource utilization, and overall architecture trade-offs. - Experience working directly with engineering-led organizations, technical founders, developers, or highly technical customer teams. - Strong customer-facing and communication skills, with the confidence to challenge assumptions and push back constructively when necessary. - Commercial awareness and an understanding that engineering capacity is a strategic resource that must be allocated thoughtfully. - Strong Python skills and familiarity with modern AI, API, MLOps, DevOps, and cloud technologies. - Experience with technologies such as OpenAI or Anthropic SDKs, LangChain, LangSmith, smolagents, FastAPI, Flask, Kubernetes, Docker, and Git is highly valuable. - Familiarity with major cloud AI platforms such as AWS SageMaker and Bedrock, Google Cloud Vertex AI, or Azure Machine Learning is preferred. - Ability to operate effectively in a fast-moving, international environment with a high degree of ownership and autonomy. ## Benefits - Competitive compensation. - Flexible remote work opportunities within Europe. - High level of ownership, autonomy, and flexibility. - Career growth and continuous learning opportunities. - Opportunity to work on impactful, technically challenging AI infrastructure projects. - Collaborative, innovative, and engineering-driven working environment. - International team with highly experienced professionals across AI, software, and infrastructure. - Opportunity to influence platform evolution and shape solutions for real-world AI workloads. - Fast-paced environment with meaningful impact and significant opportunities for professional growth.