Senior Customer AI Engineer - Token Factory
New
J
JobgetherAI infrastructure
Work remotely from within EuropeFull-TimeSenior
Salary not disclosed
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Job Details
- Required Skills
- LLMDistributed Systems
Requirements
- Have practical experience with AI inference frameworks such as vLLM, TensorRT, or similar technologies.
- Understand how inference frameworks are applied in production environments.
- Have a strong understanding of cloud infrastructure, distributed systems, or high-load applications.
- Be familiar with AI and machine learning workloads, particularly large language models, model inference, and infrastructure for serving them efficiently.
- Be able to troubleshoot complex technical issues, investigate system behavior, identify bottlenecks, and develop solutions.
- Have experience working directly with technical customers, such as software engineers, machine learning teams, or infrastructure specialists.
- Be able to explain complex technical concepts clearly and adapt communication to different audiences.
- Be able to manage multiple customer engagements, competing priorities, and technical challenges.
- Work effectively with customer engineering teams, Solution Architects, Product teams, and Infrastructure specialists.
- Hands-on experience with GPU workloads, AI infrastructure, or large-scale model inference is an advantage.
- Experience in solutions engineering, site reliability engineering, or technical support in B2B technology is an advantage.
- Experience helping customers deploy, optimize, and maintain complex cloud-based or AI-powered applications in production is an advantage.
Responsibilities
- Own customer deployments from initial proof of concept through production and ongoing scaling.
- Ensure customer AI workloads are deployed successfully, remain stable, and scale with business requirements.
- Monitor and improve inference latency, throughput, resource utilization, and cost efficiency.
- Investigate performance bottlenecks and implement solutions that improve reliability and scalability.
- Guide customer engineering teams on implementation, performance optimization, troubleshooting, and production readiness.
- Collaborate with Solution Architects and internal engineering teams to align customer requirements with platform capabilities.
- Coordinate investigation and resolution of production incidents with Product, Infrastructure, and Engineering teams.
- Identify deployment risks and opportunities to improve customer outcomes and AI infrastructure usage.
- Gather customer deployment feedback and communicate insights to Product and Infrastructure teams.
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