Machine Learning Engineer

Summary

Join Nimble Gravity's Data & AI team as a Machine Learning Engineer to design, build, and deploy intelligent models and ML-powered workflows. Collaborate with data engineers, product owners, and AI specialists to create production-ready solutions. You will focus on model development, data pipeline engineering, AI workflow integration, and applying advanced AI techniques. Responsibilities include model monitoring and optimization, establishing MLOps best practices, and agile collaboration. The ideal candidate possesses a Bachelor's or Master's degree in a related field and 3+ years of experience in machine learning engineering.

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

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