Job Information
Robert Half Machine Learning Engineer in Coral Gables, Florida
Description
The Machine Learning Engineer will be responsible for creating and implementing scalable machine learning models to support our business objectives. You will collaborate with data scientists, engineers, and product teams to build intelligent systems that process large-scale data and provide actionable insights.
Key Responsibilities:
Design, develop, and optimize machine learning models and algorithms for real-world applications.
Build and maintain scalable data pipelines to preprocess, clean, and analyze large datasets.
Deploy, monitor, and maintain machine learning models in production environments.
Collaborate with cross-functional teams to understand business requirements and translate them into ML solutions.
Conduct experiments and performance evaluations to improve model accuracy, efficiency, and scalability.
Leverage cloud platforms (AWS, Azure, GCP) and tools for model deployment and MLOps.
Document processes, methodologies, and results for reproducibility and knowledge sharing.
Stay up-to-date with the latest advancements in AI/ML technologies and incorporate them into projects as needed.
Requirements
Bachelor’s or Master’s degree in Computer Science, Data Science, Mathematics, or related fields (or equivalent experience).
Strong programming skills in Python (preferred) or similar languages.
Proficiency in machine learning frameworks and libraries such as TensorFlow, PyTorch, Scikit-learn, or Keras.
Solid understanding of ML algorithms, including supervised, unsupervised, and deep learning methods.
Experience with data preprocessing, feature engineering, and model optimization techniques.
Hands-on experience with MLOps tools (e.g., MLflow, Airflow, Docker, Kubernetes).
Familiarity with big data tools like Spark, Hadoop, or equivalent.
Knowledge of cloud platforms (AWS, GCP, Azure) for deploying and managing ML solutions.
Excellent problem-solving and communication skills.
Preferred Qualifications:
Experience with Natural Language Processing (NLP) or Computer Vision techniques.
Knowledge of reinforcement learning, generative AI, or large language models (LLMs).
Familiarity with database systems (SQL, NoSQL) and data warehousing tools.
Exposure to version control systems (e.g., Git) and CI/CD pipelines for model development.
Relevant certifications (e.g., AWS Certified Machine Learning, TensorFlow Developer Certificate).
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