Machine Learning Engineer
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Requirements
• Bachelor’s or Master’s degree in Computer Science, Data Engineering, or a related technical field. • 3+ years of experience in machine learning engineering, applied AI development, or a similar role. • Strong, hands-on experience with ML frameworks such as TensorFlow or PyTorch, from prototyping to deployment. • Familiarity with cloud platforms (AWS, Azure, or Databricks) and experience delivering solutions at scale. • Solid understanding of working with large, complex datasets spanning structured and unstructured formats. • Sharp analytical and problem-solving skills with attention to data quality and model performance metrics. • Strong communication and collaboration abilities—you’re a team player who can explain technical concepts clearly and drive projects forward. • Nimble Gravity is a team of outdoor enthusiasts, adrenaline seekers, and experienced growth hackers. We love solving hard problems and believe the right data can transform and propel growth for any organization.
Responsibilities
• Model Development: Design, train, and refine machine learning models that tackle real business problems, ensuring they scale effectively in production environments. • Data Pipeline Engineering: Build and maintain robust data ingestion, preprocessing, and transformation pipelines for diverse data sources (structured and unstructured). • AI Workflow Integration: Contribute to end-to-end ML workflows—from serving and monitoring models to evaluating and iterating on their performance. • Advanced AI Techniques: Apply state-of-the-art approaches, including transformers, LLMs, RAG, embeddings, vector databases, predictive modeling, and reinforcement learning, to push the boundaries of what’s possible. • Model Monitoring & Optimization: Support ongoing evaluation and tuning of models to improve accuracy, efficiency, and reliability in production. • MLOps: Help establish best practices for CI/CD, testing, and automated deployment of AI models. • Agile Collaboration: Partner effectively with cross-functional teams in an agile setting, contributing to sprint planning, reviews, and collaborative problem-solving.