Machine Learning Engineer

We are looking for a skilled and driven Machine Learning Engineer to join our team and take a central role in improving the quality of our large-scale geospatial data. In this role, you will be responsible for developing and deploying machine learning and data science solutions at scale. This includes extracting insights using NLP, detecting anomalies using outlier detection techniques, and quantifying data quality through advanced analytics.

You will be working across the entire ML pipeline – from model development to deployment – in a production environment. Your ability to combine software development practices with deep data science knowledge will be critical to the success of our data quality initiatives.

• Design, develop, and implement machine learning models to enhance geospatial data quality.
• Develop NLP models to extract and structure meaningful information from unstructured data sources.
• Detect anomalies and inconsistencies in datasets using statistical and machine learning-based outlier detection methods.
• Apply data science techniques to measure and quantify data accuracy, consistency, and completeness.
• Build, maintain, and scale data pipelines and ML model integration workflows for production environments.
• Take ownership of feature delivery and continuous improvement of ML products.
• Collaborate with cross-functional teams including software engineers, data engineers, and domain experts.
• Communicate insights, findings, and recommendations effectively to technical and non-technical stakeholders.

• Proven experience in machine learning and data science methodologies such as classification, feature engineering, clustering, anomaly detection, and neural networks.
• Strong programming skills in Python, Scala, or Java.
• Proficiency with machine learning frameworks and libraries such as PyTorch, TensorFlow, Keras, Scikit-learn, NumPy, and Pandas.
• Solid understanding of classical ML algorithms (SVM, Random Forest, Naive Bayes, KNN, etc.).
• Strong software engineering principles for building reliable and maintainable systems.
• Excellent analytical and problem-solving capabilities.
• Strong communication skills, both written and verbal.
• Ability to take ownership and work independently or collaboratively in a fast-paced environment.

• Domain knowledge in NLP, information retrieval, or data mining.
• Experience with geospatial data and related technologies.
• Experience in statistical modeling and building predictive models.
• Familiarity with cloud platforms (AWS, GCP, Azure) and ML deployment tools.
• Experience in containerization and orchestration tools (Docker, Kubernetes).