About the company
This position is listed on behalf of a partner company, which manages all applications and next steps. Our partner is looking for a Senior Customer AI Engineer – Token Factory based in United Kingdom.
Responsibilities
- Own the customer journey from initial proof of concept (PoC) through production deployment and ongoing scaling.
- Ensure customer AI workloads are deployed successfully, remain stable, and scale to meet evolving business requirements.
- Accelerate time-to-production and time-to-value by identifying and addressing technical obstacles throughout implementation.
- Develop a thorough understanding of customer architectures, AI use cases, and infrastructure requirements.
- Monitor and improve inference performance, including latency, throughput, resource utilization, and cost efficiency.
- Identify performance bottlenecks, investigate root causes, and implement practical solutions to improve system reliability and scalability.
- Apply best practices for inference frameworks and AI infrastructure to support efficient production workloads.
- Work directly with customer engineering teams, machine learning specialists, and other technical stakeholders to ensure successful AI deployments.
- Provide guidance on implementation best practices, performance optimization, troubleshooting, and production readiness.
- Translate complex technical challenges into clear recommendations and actionable solutions.
- Collaborate with Solution Architects and internal engineering teams to align customer requirements with platform capabilities.
- Act as a primary technical point of contact for production-related issues and deployment challenges.
- Coordinate incident investigation and resolution with internal Product, Infrastructure, and Engineering teams.
- Communicate clearly with customers during high-pressure situations, providing timely updates and driving issues toward resolution.
- Identify potential risks proactively and implement measures to minimize disruption to customer workloads.
- Identify opportunities to optimize existing deployments, improve customer outcomes, and expand the effective use of AI infrastructure.
- Gather structured feedback from real-world customer deployments and communicate insights to Product and Infrastructure teams.
- Contribute to improvements in platform capabilities, deployment practices, and operational processes.
- Help develop scalable approaches that improve the overall customer experience and support long-term adoption.
Requirements
- Practical experience with AI inference frameworks such as vLLM, TensorRT, or similar technologies, with an understanding of their application in production environments.
- Strong understanding of cloud infrastructure, distributed systems, or high-load applications, including the technical challenges associated with scalable production deployments.
- Familiarity with AI and machine learning workloads, particularly large language models (LLMs), model inference, and the infrastructure required to serve them efficiently.
- Proven ability to troubleshoot complex technical issues, investigate system behavior, identify performance bottlenecks, and develop effective solutions.
- Experience working directly with technical customers, such as software engineers, machine learning teams, or infrastructure specialists.
- Excellent communication skills, with the ability to explain complex technical concepts clearly and adapt communication to different audiences.
- A strong sense of ownership and accountability, with a focus on delivering measurable outcomes rather than simply completing assigned tasks.
- Ability to manage multiple customer engagements, competing priorities, and technical challenges in a fast-moving environment.
- A structured, proactive, and solution-oriented mindset, with the confidence to investigate problems independently and coordinate the right resources when needed.
- Strong collaboration skills and the ability to 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 environments.
- Previous experience in solutions engineering, site reliability engineering (SRE), or technical support within business-to-business (B2B) technology environments.
- Experience helping customers deploy, optimize, and maintain complex cloud-based or AI-powered applications in production.
Conditions
- Competitive compensation: An attractive compensation package aligned with your expertise and experience.
- Career growth: Opportunities to develop your technical expertise and progress professionally.