MACHINE LEARNING ENGINEER

As a member of the ML-team you will be working with a broad range of problems with one common denominator – ML will be the key ingredient.

You will have to analyze the problem at hand, come up with a solution strategy and execute it. This typically entails gaining an in-depth understanding of the challenge, understanding the available data, and then re-formulating it as an ML problem. It requires openness, creativity, and an eagerness to learn new methodology and explore new terrains.

We approach these problems as a team, meaning that you will have to be able to clearly explain your reasoning and code in order to engage the rest of us.

NOTE:
We are looking for ML Engineers with +2 years of experience with ML in production. 
To Apply we require a work VISA for Sweden or Switzerland. Currently, we do not offer sponsorships.

Our Stack

• Python / R – standard open-source libraries
• Scikit-learn and various specialized Python and R ML libraries
• Large Language Model (LLM) frameworks such as LangChain/LlamaIndex, LangGraph, CrewAI
• Cloud platforms such as AWS, GCP, and Azure
• CI/CD: DVC, Github Actions, Sagemaker/VertexAI/AzureML,
• Relational database management systems
• MLOps and LLMOps tools for model deployment and monitoring.
• Software engineering best practices, including testing, version control (Git), and containerization (Docker, Kubernetes)
• Orchestration: Airflow, AWS Step functions, etc Engineering/LLM/deployment: Kubernetes, docker, terraform

Responsibilities

• Analyzing and planning problems, solutions, and delivery with stakeholder managment, and communication with client
• Preprocessing, feature engineering, and dataset creation
• ML and LLM model development, fine-tuning, and evaluation
• Validation of results and model interpretability
• Building and optimizing data pipelines and ML/LLM infrastructure
• Developing APIs and integrating ML models into production systems
• Ensuring scalability, monitoring, and performance optimization of deployed models

Background & Skills

• MSc or Ph.D. in a quantitative field
• Excellent understanding of a broad set of ML and deep learning algorithms, including LLMs
• Strong software development skills in Python and experience with software engineering best practices
• Experience deploying ML and LLM models into production environments
• A passion for lean, clean, and maintainable code
• The desire to grow and to share insights with others

Helpful Knowledge

• Deep learning frameworks and transformer-based architectures
• LLM fine-tuning, prompt engineering, and retrieval-augmented generation (RAG)
• Data pipelining and ML/LLM infrastructure best practices
• DevOps experience, CI/CD, Kubernetes, and serverless architectures
• Experience with vector databases e.g (Pinecode, redis, and ElasticSearch) for LLM applications

About Team Modulai

At Modulai we focus 100% on solving problems with machine learning (ML). We work in teams on a project basis. We work for clients, as part of the core team in startups where we have long-time engagement as well as building our own ML products. 

Learning and teamwork are central to how we work. Everyone in the team is or will soon be a full-stack ML engineer capable of scoping and developing end-to-end ML solutions. You should be able to do end-to-end machine learning products by yourself but actually, never do it because we always work in teams. If there is data, we will do ML on it!