• Lead the transition from proof of concept to production

  • 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.

• Optimize AI workload performance and reliability

  • 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.

• Serve as a trusted technical partner

  • 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.

• Coordinate production incidents and technical risks

  • 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.

• Drive continuous improvement and customer outcomes

  • 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.

• The following experience is considered an advantage:

  • 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.

Benefits:

• Competitive compensation: An attractive compensation package aligned with your expertise and experience.

• Career growth: Opportunities to develop your technical expertise and progress professionally in a rapidly evolving AI infrastructure environment.

• Continuous learning: Exposure to advanced AI technologies, inference frameworks, and real-world production challenges.

• Remote work flexibility: Work remotely from within Europe, with the opportunity to manage your work effectively and independently.

• Ownership and autonomy: Take responsibility for customer outcomes and influence technical decisions that directly affect production success.

• Collaborative culture: Work alongside talented engineers and specialists in an international, innovative, and fast-moving environment.

• Meaningful technical impact: Help businesses move AI applications into production and improve the performance, reliability, and cost efficiency of their workloads.

• Cross-functional collaboration: Partner with customer engineering teams and internal specialists across product development, infrastructure, and AI engineering.

• Opportunity to shape AI infrastructure: Contribute insights from real-world customer deployments to improve platform capabilities and support the future of AI.