Sr Machine Learning Engineer

Sonatafy Technology

6d•Remote

About The Position

THIS OPENING IS AVAILABLE FOR CANDIDATES IN LATIN AMERICA, NOT LIMITED TO ONLY COLOMBIA. Sonatafy Technology, headquartered in Scottsdale, Arizona, is an award-winning nearshore software development company with a strong reputation. They have a dedicated in-house team of engineers, offering end-to-end software solutions and supporting client development staff augmentation. Catering to companies of all sizes and industries, including some of the world's largest brands, Sonatafy Technology is a trusted provider of nearshore enterprise-level cloud and mobile application software development services.

Requirements

Strong understanding of machine learning algorithms, statistics, feature engineering, and optimization.
Hands-on experience with building and deploying machine learning models using Python.
Experience in trust and safety/spam detection, analyzing data for threat identification.
Proficiency in setting up and managing cloud infrastructure for model deployment.
Independent problem-solver with a creative and experimental mindset.
Working knowledge of Natural Language Processing (NLP).
Familiarity with LLM tooling — prompt engineering, embeddings, and vector databases, among others.
Machine Learning Libraries: Scikit-Learn, TensorFlow, PyTorch.
Data Manipulation: Pandas, NumPy, NLTK, spaCy.
Cloud Infrastructure: AWS and/or Azure, or similar cloud platforms.
Web Frameworks for API Deployment: Django or Flask for building APIs to serve machine learning models in production.

Responsibilities

Design and build machine learning models from scratch using algorithms such as Decision Trees, K-Means, Neural Networks, and Random Forests.
Deploy machine learning models manually using Python, ensuring scalability, reliability, and performance.
Analyze data points such as IP addresses, locations, and user behavior to identify patterns related to spam detection and trust/safety.
Perform feature engineering and selection to build effective machine-learning models.
Set up and manage cloud infrastructure to deploy machine learning models without relying on managed services such as Amazon SageMaker.
Innovate and experiment with solutions independently, taking ownership of complex problems without constant supervision.