MLOps Engineer

Our team is looking for a MLOps Engineer.

We are building the infrastructure behind cutting-edge Large Language Models (LLMs) and are looking for a curious and passionate MLOps Engineer to join our team. You will tackle exciting challenges such as processing large datasets, orchestrating training pipelines, and developing robust monitoring systems and tools to support our ML teams.

Your work will play a key role in powering LLM innovations, whether by creating data pipelines, developing tools to accelerate research, or ensuring the reliability of our systems. This is an opportunity to make a significant impact in a dynamic environment from day one.

Role and Responsibilities:

• Dive into the heart of ML infrastructure, learning how to build and maintain the systems that power our LLMs development.
• Work with cutting-edge tools and technologies that most engineers only read about in tech blogs.
• Turn complex ML workflows into streamlined, automated processes.
• Learn the ins and outs of LLM infrastructure.
• Collaborate with researchers, engineers and DevOps teams to design and execute our ML infrastructure roadmap.
  
  

• A Computer Science degree (or equivalent experience) and a hunger to learn.
• Some experience with Python and a proficiency in at least one of the following languages: Python, Java, C# and C++.
• Familiarity with Kubernetes and containers (Docker) - don't worry if you're not an expert yet!
• A problem-solver's mindset and enthusiasm for tackling complex technical challenges.
• Strong communication skills and eagerness to work in a collaborative environment.
• A service-oriented mindset, with a natural drive to understand users' needs and proactively offer solutions.
  
  

• Knowledge of any ML concepts, ML frameworks (PyTorch, TensorFlow, or similar) and ML experiment tracking tools (MLflow, Weights & Biases, etc.).
• Basic understanding of cloud platforms (AWS/GCP) or CI/CD pipelines.
• Previous projects or internships in ML infrastructure.
• Experience working on projects that operate at scale (e.g., handling large datasets, supporting multiple users, or managing distributed systems)