Machine Learning Engineer

1 week ago

Hyderabad, Telangana, India Talent Aura Full time


Required Skills and Knowledge:

  • C++ with Machine Learning
  • TensorFlow Lite, PyTorch, ONNX, Snapdragon Neural Processing Engine/Qualcomm Neural Processing SDK (any one)
  • Customize AI models to run efficiently on various hardware models like GPU, DSP, NPU, etc
  • Develop Android applications using Java or Kotlin
  • Familiarity with Android Multimedia frameworks such as Vulkan and OpenGL

Job Description:

Data Collection and Preparation:

  • Data Gathering: Collect and aggregate data from different sources like databases, APIs, and external datasets
  • Data Cleaning: Preprocess and cleanse data to maintain quality and consistency, including handling missing values, outliers, and duplicates
  • Data Transformation: Normalize data and perform necessary transformations for analysis and model training, which may involve feature scaling, encoding categorical variables, and creating new features

Exploratory Data Analysis (EDA):

  • Data Visualization: Utilize visualization tools to comprehend data distributions, relationships, and patterns
  • Statistical Analysis: Conduct statistical analysis to reveal trends, correlations, and insights in the data

Model Development:

  • Algorithm Selection: Choose suitable machine learning algorithms based on the problem type (e.g., classification, regression, clustering)
  • Model Training: Train machine learning models using training data, optimizing for accuracy and performance
  • Hyperparameter Tuning: Adjust model hyperparameters to enhance performance and prevent overfitting
  • Model Evaluation: Assess model performance using relevant metrics (e.g., accuracy, precision, recall, F1 score) and validate using cross-validation techniques

Deployment and Integration:

  • Model Deployment: Implement machine learning models in production environments to support real-time data and scalability
  • API Development: Create APIs to integrate machine learning models with various systems and applications
  • Monitoring: Continuously monitor model performance, identifying and resolving issues like drift or degradation

Optimization and Maintenance:

  • Model Optimization: Fine-tune models for efficiency, speed, and resource optimization
  • Model Retraining: Update models with new data or when performance deteriorates
  • Documentation: Document model architecture, training processes, and deployment procedures for future reference and reproducibility

Collaboration and Communication:

  • Team Collaboration: Work closely with data scientists, data engineers, software developers, and stakeholders to align on project objectives and requirements
  • Stakeholder Communication: Clearly communicate findings, model performance, and potential impacts to non-technical stakeholders

Research and Development:

  • Stay Updated: Stay informed about the latest advancements in machine learning and AI technologies
  • Experimentation: Try out new algorithms, tools, and techniques to enhance model performance and tackle challenging problems

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