Modulai AB

About Modulai Modulai is an opinionated, no-bullshit AI partner. We turn AI into real business impact. No fluff, just results. We're a leading Nordic AI/ML consultancy with deep expertise in Generative AI, Machine Learning, MLOps, LLMOps and Agentic AI. We work with global enterprises and startups across industries, from banking and healthcare to media and manufacturing. Ranked as one of the fastest growing AI Consultancies in Sweden, Modulai is now joining Analysys Mason - the leading specialist consultancy, creating lasting value in technology-intensive industries. Which has a strong presence in Oslo and ongoing engagements across Norway. Together we're establishing a Modulai hub in Oslo, and we're looking for the person to shape and lead it. The role This is a key strategic hire. Together with the Swedish team, you'll establish Modulai in Norway as our senior technical lead on the ground in Oslo, building client relationships and our reputation in the Norwegian market. The Oslo hub works as one team with our engineering teams in Göteborg and Stockholm. You'll own client relationships and technical direction locally, and you'll lead delivery teams across all three cities. You'll also work closely with Analysys Mason colleagues, combining their client relationships and industry expertise with Modulai's AI capabilities to create and deliver joint opportunities. You're a senior ML engineer who is as comfortable in a boardroom as in a codebase. You'll lead projects from the first client conversation through to production. You'll mentor and inspire the team, and you'll explain your reasoning clearly to engineers, clients and decision-makers alike. What you'll do * Establish Modulai's presence in Norway by building the client base, network and reputation from Oslo * Collaborate with Analysys Mason to identify joint opportunities and deliver integrated AI engagements * Bridge Oslo, Stockholm and Göteborg by leading cross-office delivery teams * Act as a trusted technical advisor to senior stakeholders, from idea to execution * Shape new opportunities and support pre-sales, scoping and workshops * Lead end-to-end ML development, from data and feature engineering to models, validation and infrastructure * Mentor the team in state-of-the-art applied ML * Represent Modulai at industry events and forums in Norway What we're looking for * MSc or PhD in a quantitative field * 5+ years leading end-to-end ML projects, including ML infrastructure and deployment * Strong software development skills and a broad understanding of ML algorithms and frameworks * Client-facing or commercial experience, e.g. consulting, pre-sales, product ownership or advisory work * The ability to discuss AI's business value convincingly with decision-makers * An entrepreneurial mindset and experience leading teams across locations * Excellent communication skills, in person, on stage and in writing * A passion for lean, clean and maintainable code * Fluent in English and Norwegian (or another Scandinavian language) Nice to have * A professional network in the Norwegian tech or business community * Experience establishing a new market or client base * Hands-on experience with GenAI (LLMs, multi-modal and vision models) * Deep learning, DevOps, CI/CD and Kubernetes experience Our stack: Python, scikit-learn and specialized ML libraries, GCP/AWS/Azure, relational databases and Apache Spark. Why join Modulai? * Take a key role in establishing Modulai in Norway, with real influence over the Oslo hub * Combine Modulai's AI expertise with Analysys Mason's reach and industry depth * Be part of one connected Nordic team across Oslo, Stockholm and Göteborg * A culture built on trust, collaboration and pragmatic innovation Details: Based in Oslo, with flexible hours and regular travel to Stockholm and Göteborg. You must be eligible to work in Norway; we don't offer visa sponsorship. Interested? Apply today and let's build the future of AI together.

Modulai works with fish, trains, clothes, money, pets, office spaces, sound sensors and much more. If there is data, we do ML (Machine Learning) on it. Our team consists of devoted ML engineers with strong track records from some of Sweden’s most successful startups. We work on project basis and take end-to-end responsibility. We love ML and we think that the best way for us to expand our knowledge is to be exposed to a diversified set of challenging and fun projects. 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. The projects could be external as well as internal – and in all cases – delivery is central. You will have to analyze the problem at hand, come up with a solution strategy and execute on it. This typically entails gaining an in-depth understanding of the challenge, understanding the available data and then re-formulating it as a ML problem. It requires openness, creativity and an eagerness to learn new methodology and exploring new terrains. We frequently attack 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 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 for large enterprises transforming ways of working. 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! Apply here: https://modulai.teamtailor.com/

SENIOR MACHINE LEARNING ENGINEER We are recruiting Senior Machine Learning Engineers to work on the development of a next-generation fraud detection platform for a major Payment Service Provider (PSP). The role combines production-grade machine learning engineering, advanced data analysis/statistics, and customer-facing technical collaboration. You will work closely with the client’s data, engineering, risk, and compliance teams to design, implement, deploy, and continuously improve real-time ML models operating in a highly regulated financial environment. 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. This is a hands-on, forward-deployed role requiring both deep technical expertise and strong communication skills in English. Core Responsibilities * Design, train, evaluate, and deploy ML models for transaction-level fraud detection (primarily tabular data). * Analyze large-scale transaction datasets to identify patterns, leakage, bias, and data quality issues. * Build and maintain production ML services (real-time and batch). * Implement robust ML pipelines, model monitoring, and experiment frameworks. * Collaborate directly with client engineers, data scientists, and risk teams. * Translate complex technical concepts and results into clear, actionable insights for technical and non-technical stakeholders. * Operate within strict requirements for reliability, explainability, traceability, and compliance. Background and skills: * Production-grade Python and solid ML fundamentals (XGBoost/LightGBM, Scikit-learn, feature engineering, imbalanced datasets) * Experience building and shipping ML-powered APIs (FastAPI/Flask), Docker, CI/CD, and distributed data processing (PySpark/SQL) * Strong stats foundation: experimental design, bias/leakage detection, time-dependent validation * Hands-on MLOps experience — feature stores, Airflow/Kubeflow, model monitoring, real-time inference, A/B testing * MSc or Ph.D. in a quantitative field * Excellent understanding of a broad set of ML algorithms and frameworks * A passion for lean, clean, and maintainable code * The desire to grow and to share insights with others Domain experience: Fraud detection, payments, fintech, or credit risk. You've worked with cost-sensitive decisions, highly imbalanced data, and models that directly impact business risk. How you work: You communicate clearly with engineers, product, and compliance stakeholders alike. You write good documentation and can hold your own in architecture discussions. About Team Modulai At Modulai, we focus 100% on solving problems with machine learning (ML). We work in teams on a project basis, for clients, as part of the core team in startups where we have long-term engagements, and we also build 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 never do it because we always work in teams. If there is data, we will do ML on it! Note: Due to the summer holiday period, applications will be reviewed starting 27th of July.