Machine Learning Engineer

2 weeks ago

Hyderabad, Telangana, India Talent Aura Full time

Required Skills and Knowledge :

- C++ with ML

- TFLite, PyTorch, ONNX, SNPE/QNN. (any one)

- Customize the AI models to run on different hardware models (GPU, DSP, NPU etc)

- Android application development using Java/Kotlin

- Android Multimedia frameworks like Vulkan, OpenGL

Job Description :

Data Collection and Preparation :

- Data Gathering : Collect and aggregate data from various sources, such as databases, APIs, and external datasets.

- Data Cleaning : Preprocess and clean the data to ensure quality and consistency. This includes handling missing values, outliers, and duplicates.

- Data Transformation : Transform and normalize data to make it suitable for analysis and model training. This may involve feature scaling, encoding categorical variables, and generating new features.

Exploratory Data Analysis (EDA) :

- Data Visualization : Use visualization tools and techniques to understand data distributions, relationships, and trends.

- Statistical Analysis : Perform statistical analysis to identify patterns, correlations, and insights from the data.

Model Development :

- Algorithm Selection : Select appropriate machine learning algorithms based on the problem type (e.g., classification, regression, clustering).

- Model Training : Train machine learning models using training data, optimizing them for accuracy and performance.

- Hyperparameter Tuning : Fine-tune model hyperparameters to improve performance and prevent overfitting.

- Model Evaluation : Evaluate model performance using appropriate metrics (e.g., accuracy, precision, recall, F1-score) and validate with cross-validation techniques.

Deployment and Integration :

- Model Deployment : Deploy machine learning models into production environments, ensuring they can handle real-time data and scale as needed.

- API Development : Develop APIs to integrate machine learning models with other systems and applications.

- Monitoring : Continuously monitor model performance in production, identifying and addressing issues such as drift or degradation.

Optimization and Maintenance :

- Model Optimization : Optimize models for efficiency, speed, and resource utilization.

- Model Retraining : Retrain models as new data becomes available or as model 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 other stakeholders to align on project goals and requirements.

- Stakeholder Communication : Communicate findings, model performance, and potential impacts to non-technical stakeholders in a clear and concise manner.

Research and Development :

- Stay Updated : Keep up-to-date with the latest advancements in machine learning and AI technologies.

- Experimentation : Experiment with new algorithms, tools, and techniques to enhance model performance and solve complex problems.

(ref:hirist.tech)

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