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
