ML Engineer – Machine Learning & Geospatial Data - 20093

Assignment Description

We are looking for an ML Engineer to develop and deploy machine learning systems that improve the quality of large-scale geospatial datasets. You will build classical machine learning and NLP models to extract structured information, detect anomalies, and measure data quality.

The role covers the full development lifecycle, from experimentation and model development to production integration and deployment at scale. Strong Python programming, data science expertise, and software engineering skills are essential.

What You Will Work On

• Design, develop, test, and deploy machine learning models.
• Build and maintain scalable data pipelines and software solutions.
• Apply data science techniques to improve data quality and generate insights.
• Work with LLMs, prompting techniques, and agent-based workflows.
• Develop models for classification, clustering, feature engineering, and anomaly detection.
• Integrate ML models into production systems.
• Own features and improvements from development through delivery.
• Collaborate with cross-functional teams to deliver reliable solutions.

What You Bring

• Strong Python programming skills.
• Proven experience in machine learning and data science.
• Solid understanding of classical ML algorithms, including SVM, Random Forest, Naive Bayes, and k-NN.
• Experience with LLMs, prompting, and agent-based workflows.
• Hands-on experience with PyTorch, TensorFlow, Keras, Scikit-learn, NumPy, and Pandas.
• Strong analytical thinking and problem-solving skills.
• Ability to balance innovative approaches with practical software delivery.
• Excellent written and verbal communication skills.
• A proactive mindset, strong ownership, and the ability to work in a fast-paced environment.

Meritorious

• Experience with Scala or Java.
• Knowledge of NLP, information retrieval, or data mining.
• Experience in statistical modelling and predictive model development.

Interested? Please share your updated CV at cv@veritaz.se to proceed further.