AI/ML Engineer

2 weeks ago

Mountain View, CA, United States Bayone Full time
Key Responsibilities
  • Develop, train, test, and deploy machine learning models for classification, prediction, recommendation, NLP, or computer vision use cases.
  • Build scalable data pipelines and model training workflows using Python and modern ML frameworks.
  • Collaborate with data engineers, product managers, and software developers to deliver end-to-end AI solutions.
  • Conduct data exploration, feature engineering, and model evaluation using statistical techniques and ML best practices.
  • Implement MLOps practices including model versioning, monitoring, CI/CD, and automated deployment.
  • Optimize models for performance, latency, and scalability in production environments.
  • Work with cloud platforms (AWS/GCP/Azure) to manage compute, storage, and model-serving infrastructure.
  • Document models, experiments, and processes to ensure reproducibility.
  • Stay current with emerging AI/ML research, tools, and industry trends.
Required Skills & Qualifications
  • Bachelor's or Master's degree in Computer Science, Data Science, Machine Learning, or related field.
  • Strong proficiency in Python and libraries such as TensorFlow, PyTorch, Scikit-learn, NumPy, and Pandas.
  • Experience developing ML models end-to-end including data preparation, training, and evaluation.
  • Solid understanding of algorithms, statistics, and machine learning fundamentals.
  • Hands-on experience with cloud ML services (AWS Sagemaker, GCP Vertex AI, Azure ML).
  • Knowledge of software engineering principles: version control (Git), testing, CI/CD.
  • Familiarity with MLOps tools such as MLflow, Kubeflow, or DVC.
  • Ability to work with large datasets and distributed systems.
Preferred Qualifications
  • Experience with deep learning architectures (CNNs, RNNs, Transformers).
  • Knowledge of NLP, computer vision, or generative AI techniques.
  • Exposure to containerization (Docker, Kubernetes).
  • Experience building real-time inference systems or model APIs.
  • Published work, Kaggle participation, or open-source contributions in ML/AI.

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