Position: Machine Learning (ML) Engineer
Location: Dallas, TX/ Atlanta /GA/ Plano, TX onsite day1 (Any of these three locations can be given)
Client: TechM
Visa : GC/USC/H4EAD /H1B WITH I 94 copy
Note : We need I94 Copy For Submission.
AI, data science, Machine Learning, Computer Vision and Cloud computing This is a senior level role where this person is responsible for the development of high performance, distributed modeling tasks using Machine Learning and Data Science
A Machine Learning (ML) Engineer plays a crucial role in designing, implementing, and maintaining machine learning models and systems. They bridge the gap between data science and software engineering, ensuring that ML models are scalable, efficient, and integrated into production environments.
Key Roles and Responsibilities
- Algorithm Selection: Select and implement appropriate machine learning algorithms and models based on the problem and data characteristics.
- Feature Engineering: Develop and transform features from raw data to improve model performance. This includes data preprocessing, normalization, and feature selection.
- Model Training: Train machine learning models using historical data, optimizing model parameters to achieve the best performance.
- Model Evaluation: Evaluate model performance using metrics such as accuracy, precision, recall, F1 score, AUC-ROC, and others. Compare different models and select the best-performing one.
- Hyperparameter Tuning: Optimize model hyperparameters using techniques such as grid search, random search, or Bayesian optimization to improve model performance and generalizability.
- Cross-Validation: Implement cross-validation techniques to ensure the robustness and reliability of the model.
- Model Deployment: Deploy machine learning models into production environments, ensuring they are scalable, efficient, and reliable.
- API Development: Develop APIs to expose machine learning models as services that can be consumed by other applications or systems.
- Integration: Integrate machine learning models with existing systems, applications, or workflows. Collaborate with software engineers and IT teams to ensure seamless deployment and integration.
- Model Monitoring: Monitor the performance of deployed models in real-time, tracking metrics such as latency, throughput, and prediction accuracy. • Model Maintenance: Update and retrain models as new data becomes available to ensure they remain accurate and relevant.
Address issues such as model drift and data drift.
- Error Analysis: Analyze model errors and misclassifications to identify areas for improvement and refine the model.
- Infrastructure Management: Set up and manage the infrastructure required for training and deploying machine learning models, including cloud platforms, GPUs, and distributed computing resources.
- Automation: Automate repetitive tasks such as data preprocessing, model training, and deployment using scripting languages (e.g., Python) and workflow orchestration tools (e.g., Apache Airflow).
- Tooling: Utilize and maintain ML frameworks and libraries such as TensorFlow, PyTorch, scikit-learn, and others to streamline the development and deployment process.
- Cross-Functional Collaboration: Work closely with data scientists, software engineers, product managers, and other stakeholders to understand their requirements and ensure alignment on project goals.
- Documentation: Create and maintain comprehensive documentation for machine learning models, pipelines, and processes. Ensure documentation is accessible and up-to-date.
- Stakeholder Communication: Communicate progress, issues, and solutions effectively with stakeholders.
Provide regular updates on machine learning activities and projects.