Senior ML Solutions Architect - Token Factory

Accountabilities

• Optimize LLM inference workflows across different modalities to deliver measurable business value and meet customer requirements.
• Support customers with supervised and reinforcement-learning-based fine-tuning approaches to improve model quality and performance.
• Design and implement LLM-powered solutions using serverless inference services and served open-source models.
• Build production-ready applications using LLM APIs, including multimodal models covering text, vision, audio, and domain-specific use cases.
• Provide technical guidance on prompt engineering, RAG architectures, model selection, inference optimization, and deployment strategies.
• Guide customers through the transition from proof of concept to production, with a focus on performance, reliability, scalability, and cost efficiency.
• Work closely with product and engineering teams to communicate customer needs, identify platform gaps, and contribute to roadmap development.
• Help customers select appropriate models, inference configurations, and fine-tuning strategies based on their use cases and technical constraints.
• Contribute to improving the platform and its capabilities by sharing practical insights from customer implementations and production workloads.

Requirements:

• 5+ years of professional experience working with ML/AI systems, including at least 2 years focused specifically on LLMs and generative AI.
• Deep understanding of the modern LLM ecosystem, including model architectures, inference approaches, and fine-tuning techniques.
• Hands-on experience running LLMs in production, including deploying and operating inference workloads at scale.
• Strong practical experience with LLM fine-tuning, including supervised fine-tuning, SFT, LoRA, and data preparation or curation; experience with reinforcement-learning-based fine-tuning is a strong advantage.
• Experience building LLM evaluation frameworks, including task-specific benchmarks, offline and online evaluation pipelines, and LLM-as-a-judge approaches.
• Practical experience with modern inference frameworks and ML libraries such as vLLM, SGLang, TensorRT-LLM, or Transformers.
• Experience deploying LLM-powered applications through APIs from providers such as OpenAI or Anthropic, as well as open-source models.
• Strong Python programming skills and the ability to develop practical, production-oriented AI solutions.
• Excellent communication skills, with the ability to explain complex technical concepts clearly to customers, engineers, product teams, and other audiences.
• Experience working with multimodal AI models, such as vision-language or speech models, is a plus.
• Familiarity with DevOps technologies including Docker, Kubernetes, and Git is beneficial.
• Contributions to open-source ML or AI projects are an additional advantage.
• Familiarity with cloud AI platforms such as AWS SageMaker or Bedrock, Google Vertex AI, or Azure ML is welcome.

Benefits:

• Competitive compensation.
• Career growth and ongoing learning opportunities.
• Flexible working arrangements with a high degree of autonomy and ownership.
• Fully remote work opportunity from Europe.
• Collaborative and innovative international working environment.
• Opportunity to work on impactful AI infrastructure and production-grade LLM projects.
• Exposure to advanced technologies across LLM inference, fine-tuning, evaluation, retrieval, and multimodal AI.
• Opportunity to work closely with talented AI, engineering, product, and customer-facing teams.
• Meaningful technical ownership and the opportunity to influence the evolution of an emerging AI platform.
• Inclusive workplace committed to equal employment opportunities and a diverse working environment.
• Reasonable accommodations are available during the application process where required.
• Candidates must be authorized to work in the country where they apply and may need to provide proof of employment eligibility.