Senior ML Solutions Architect - Token Factory
New
J
JobgetherAI Infrastructure
Work remotely across EuropeFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 5+ years of professional experience working with ML/AI systems, including at least 2 years focused specifically on LLMs and generative AI.
- Required Skills
- PythonLLMGenerative AI
Requirements
- 5+ years of professional experience working with ML/AI systems.
- At least 2 years of experience specifically focused 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 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.
Responsibilities
- 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.
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