Senior ML Solutions Architect - Token Factory

Slashhash; Netherlands; Hybrid; EUR 110,000 - 150,000; Full time

Job summary

Slashhash is seeking a Senior ML Solutions Architect to join the partner company's AI infrastructure team in Europe. You will architect a serverless platform for running and customizing open-source LLMs in production, focusing on optimized inference, fine-tuning, evaluation, and RAG architectures.

The role requires 5+ years in ML/AI with 2+ years in LLMs and generative AI, strong Python skills, and a customer-facing mindset to engage European clients.

Qualifications

• 5+ years of ML/AI experience, including 2+ years focused on LLMs and generative AI.
• Strong Python programming skills.
• Experience designing optimized LLM inference workflows and evaluation.
• Familiarity with serverless inference architectures and production readiness.

Responsibilities

• Optimize LLM inference workflows across modalities to deliver measurable value and meet customer requirements.
• Support customers with supervised and reinforcement-learning-based fine-tuning approaches to improve quality.
• 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 (text, vision, audio).
• Provide technical guidance on prompt engineering, RAG architectures, model selection, and deployment strategies.
• Guide customers from proof-of-concept to production with focus on performance, reliability, and cost efficiency.
• Work with product and engineering to communicate customer needs and roadmap gaps.
• Advise on model selection and tuning strategies based on use cases and constraints.

Skills

Machine Learning

LLMs

Generative AI

Python

Fine-tuning

Supervised Fine-Tuning

LoRA

Reinforcement Learning

LLM Evaluation Frameworks

vLLM

SGLang

TensorRT-LLM

Transformers

OpenAI API

Anthropic API

RAG

Prompt Engineering

Serverless Inference

Multimodal Models

Tools

Docker

Kubernetes

Git

Open-source Contributions

AWS SageMaker

AWS Bedrock

Google Vertex AI

Azure ML

Job description

Senior ML Solutions Architect needed for a partner company's AI infrastructure team, building a serverless platform for running and customizing open-source LLMs in production. Requires 5+ years of ML/AI experience including 2+ years focused on LLMs and generative AI, with strong Python skills. Role involves designing optimized inference workflows, fine-tuning, evaluation, and RAG architectures while partnering directly with customers across Europe.

Technical (Must-have)

• Machine Learning
• LLMs
• Generative AI
• Python
• Fine-tuning
• Supervised Fine-Tuning
• LoRA
• Reinforcement Learning
• LLM Evaluation Frameworks
• vLLM
• SGLang
• TensorRT-LLM
• Transformers
• OpenAI API
• Anthropic API
• RAG
• Prompt Engineering
• Serverless Inference
• Multimodal Models

Soft Skills

• Communication
• Customer-facing
• Collaboration
• Technical guidance
• Problem solving

Technical (Nice-to-have)

• Vision-Language Models
• Speech Models
• Docker
• Kubernetes
• Git
• Open-source Contributions
• AWS SageMaker
• AWS Bedrock
• Google Vertex AI
• Azure ML

Key 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.
• 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.

Machine Learning, LLMs, Generative AI, Python, Fine-tuning, Supervised Fine-Tuning, LoRA, Reinforcement Learning, LLM Evaluation Frameworks, vLLM, SGLang, TensorRT-LLM, Transformers, OpenAI API, Anthropic API, RAG, Prompt Engineering, Serverless Inference, Multimodal Models, 5 years of relevant experience