Machine Learning Engineer

At our consulting company, the Machine Learning Engineer is responsible for building and scaling AI/ML solutions, designing and maintaining data infrastructure, writing clean code, and creating reusable ML components. Additionally, you will collaborate with cross-functional agile teams, ensuring that your work contributes to impactful business outcomes.

To qualify for the Machine Learning Engineer position at our consulting company in Stockholm, you should have over 5 years of experience in ML engineering, with a strong focus on production-ready ML systems. Familiarity with Python, cloud-native applications, and MLOps practices, along with a collaborative spirit and problem-solving mindset, is essential.

Our consulting company is committed to fostering professional growth. We provide opportunities for you to take real responsibility and develop your skills in a dynamic environment. With ongoing collaboration with experienced team members and access to various projects, you can advance your career significantly while working on exciting challenges.

As a Machine Learning Engineer at our consulting company, you will work with technologies such as Python for coding, SQL and DBT for handling data, and cloud platforms like GCP/Vertex AI for deploying machine learning solutions. You’ll also manage both batch and streaming data to support robust ML pipelines.

Our consulting company boasts an inclusive and welcoming workplace filled with passionate technology enthusiasts. We value each individual's contribution, promote collaboration, and encourage a diverse mix of knowledge and ideas. This creates a vibrant work culture where your uniqueness is celebrated as part of our collective success.

To design a Machine Learning system from scratch, I would start with data exploration to understand the data's structure and identify potential features. Next, I would focus on feature engineering to enhance model performance, followed by choosing the right algorithms based on the problem type. Finally, I would develop the infrastructure for deployment, ensuring monitoring, scalability, and proper documentation.

In my previous roles, I've utilized cloud platforms like GCP and Vertex AI extensively for deploying and managing machine learning models. I have experience in setting up cloud infrastructure, optimizing resource usage, and leveraging cloud-native tools such as BigQuery for data processing, which enhances collaboration and scaling capabilities.

Ensuring code quality involves following best practices such as writing modular, reusable code, conducting peer code reviews, and utilizing automated testing frameworks. I also emphasize thorough documentation throughout the development process, which helps maintain clarity and facilitates collaborative improvements.

One challenging ML problem I solved involved demand forecasting for a supply chain client. I had to integrate multiple data sources and employ various algorithms to improve prediction accuracy. By iterating through model selection and tuning processes, I successfully enhanced the forecast accuracy, reducing inventory costs significantly for the client.

I utilize several feature engineering techniques, such as normalization, one-hot encoding for categorical variables, and creating interaction features to capture complex relationships. Dimensionality reduction techniques like PCA can also be beneficial in improving model performance while reducing overfitting.

MLOps is crucial for operationalizing machine learning models. My experience includes designing ML pipelines that automate the process of data ingestion, training, and deployment. By implementing MLOps practices, I ensure that models are continuously improved, monitored, and deployed efficiently, which streamlines collaboration between data science and operations teams.

I stay updated with the latest trends in machine learning by following reputable ML blogs, participating in webinars, and attending industry conferences. Engaging with the community on platforms like GitHub and attending meetups helps me learn from peers and stay informed about emerging technologies and methodologies.

Data visualization is vital in machine learning as it helps in understanding patterns and insights from data. It allows stakeholders to visualize relationships and trends, which can facilitate better decision-making. I use libraries like Matplotlib and Seaborn for visual analytics to effectively communicate the findings of my models.

In an agile team environment, I prioritize collaboration and open communication. I participate actively in daily stand-ups, share progress, and solicit feedback from team members. Adapting to changes quickly and iterating on feedback are key principles I adhere to, which helps in achieving common goals effectively.

To handle imbalanced data, I use techniques such as resampling methods (over-sampling the minority class or under-sampling the majority class), applying appropriate loss functions that penalize mislabeling of minority classes, and exploring ensemble methods like SMOTE. These approaches help build robust models that generalize better across varying data distributions.