Machine Learning Engineer

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

About Modulai

We are a leading machine learning agency founded in 2018. We help ambitious organisations - from early-stage startups to world-leading enterprises - create real value with the latest ML breakthroughs and research to put models in production. From healthcare and finance to retail, logistics, and manufacturing, Modulai partners with industries across the board to unlock the true potential of their data.

At Modulai, curiosity is a core strength. Our ML team is deeply collaborative, we believe in sharing knowledge and constantly pushing the boundaries of what ML can achieve. Together, we achieve new levels of competitiveness and business growth. ML should always be a force for good - everything we build aims to make a positive and lasting impact. If there is data, we will do ML on it.

About the role

You will 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 reformulating 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.

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 management, and communication with client

• Preprocessing, feature engineering, and dataset creation

• ML and LLM model development, fine-tuning, and evaluation

• Using Cloud platforms such as AWS, GCP, and Azure

• Validation of results and model interpretability

• Building and optimizing data pipelines and ML/LLM infrastructure

• Software engineering best practices, including testing, version control (Git), and containerization (Docker, Kubernetes)

• Developing APIs and integrating ML models into production systems

• Ensuring scalability, monitoring, and performance optimization of deployed models

Background and skills

• MSc or Ph.D. in a quantitative field

• +2 years of experience with ML in production.

• 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 going into this role

• Deep learning frameworks and transformer-based architectures

• Experience in MLOps and LLMOps tools for model deployment and monitoring.

• 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

• Experience with handling client relationships and delivery

NOTE:

To apply, we require a work VISA for Sweden. Currently, we do not offer sponsorships.

Why join Modulai?

• Healthy work-life balance and a flexible work environment

• Competitive benefits like yearly health checks, ski trip and kickoff

• Curious, diverse and knowledge-sharing culture

• Work on real, critical problems with cutting-edge AI

• A place to grow and develop your AI expertise