Description:
Role: Data Engineer - Artificial Intelligence & Machine Learning
Location Options: Bay Area - CA
Responsibilities: -
1. Develop AI/ML Models:
• Design, build, and train machine learning models using appropriate algorithms (e.g., supervised, unsupervised, reinforcement learning, deep learning).
• Use various machine learning and AI frameworks (TensorFlow, PyTorch, Scikit-learn, Keras, etc.) to implement models.
• Experiment with different approaches (e.g., decision trees, neural networks, ensemble methods) and optimize for the best performance.
• Perform model selection, training, tuning, and validation using real-world data to achieve the most accurate results.
2. Data Preparation & Feature Engineering:
• Clean, preprocess, and structure raw data for analysis, ensuring it is suitable for model training.
• Implement data augmentation techniques, handle missing data, and remove outliers.
• Engineer features that will improve model performance, understanding the data's underlying relationships.
3. Algorithm Design and Optimization:
• Develop and optimize algorithms for specific use cases like image recognition, natural language processing (NLP), speech recognition, or recommendation systems.
• Optimize algorithms for high efficiency, scalability, and real-time performance.
• Regularly assess and improve model accuracy by experimenting with various hyperparameters, architectures, and optimization techniques.
4. Deploy Machine Learning Models:
• Collaborate with software engineers to deploy machine learning models into production environments, integrating them with existing systems.
• Ensure the models are scalable, performant, and able to handle real-time or batch data as required.
• Implement model monitoring and performance tracking tools to evaluate accuracy and detect any model drift over time.
5. Model Evaluation & Testing:
• Use cross-validation and other techniques to evaluate the model's generalization capabilities.
• Implement performance metrics to measure model accuracy, precision, recall, F1-score, and other relevant metrics based on project needs.
• Perform A/B testing and compare the performance of multiple models.
6. Continuous Improvement & Research:
• Stay up-to-date with the latest AI/ML research and advancements in the field, such as new algorithms, architectures, and technologies.
• Participate in code reviews and contribute to best practices in AI/ML development.
• Experiment with new AI and machine learning techniques to continually improve performance and solve complex problems.
7. Collaboration & Communication:
• Work closely with Data Scientists, Software Engineers, Product Managers, and other stakeholders to understand business problems and translate them into machine learning tasks.
• Communicate findings, insights, and progress to non-technical stakeholders in a clear, understandable manner.
• Collaborate on projects, providing expertise on AI/ML concepts to help shape product features or solutions.
8. Ethical Considerations and Bias Mitigation:
• Ensure that the models and algorithms are free from biases and ethically sound, particularly when dealing with sensitive data.
• Evaluate fairness, transparency, and interpretability of models, especially in critical applications like healthcare, finance, and legal sectors.
9. Documentation:
• Document the model-building process, algorithm choice, and data used, ensuring reproducibility and transparency.
• Write clear technical documentation and user guides to facilitate collaboration and knowledge transfer.
10. Innovation and Prototyping:
• Prototype AI-driven solutions to demonstrate their potential and feasibility.
• Develop proof of concepts (PoCs) and new algorithms for emerging AI and ML technologies (e.g., federated learning, reinforcement learning, generative models)
Qualifications:
1.Educational Background:
Bachelor's or Master's degree in Computer Science, Data Science, Artificial Intelligence, Mathematics, or a related field.
Ph.D. in a relevant field is a plus but not required
2.Technical Skills:
• Strong programming skills in Python, R, or similar languages for machine learning and data analysis.
• Deep knowledge of machine learning libraries such as TensorFlow, PyTorch, Scikit-learn, Keras, and XGBoost.
• Strong foundation in linear algebra, probability, statistics, and optimization techniques.
• Proficiency in algorithms and data structures.
• Experience with deep learning techniques, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformers, and reinforcement learning.
• Familiarity with NLP techniques, computer vision, time series analysis, and other AI sub-domains.
• Knowledge of data preprocessing, feature extraction, and feature selection techniques.
• Proficiency in cloud platforms (AWS, Azure, Google Cloud) for model training and deployment.
• Experience with containerization and orchestration tools (e.g., Docker, Kubernetes) is a plus.
• Familiarity with big data technologies (Hadoop, Spark, etc.) is a plus.
3.Soft Skills:
• Strong problem-solving and analytical skills.
• Ability to work in a collaborative, cross-functional environment.
• Effective communication skills to explain complex AI/ML concepts to non-technical stakeholders.
• Strong attention to detail and ability to troubleshoot and debug code.
• Passion for continuous learning and staying up-to-date with AI and ML advancements.
4.Experience:
• Proven experience (3+ years) in developing and deploying machine learning or AI models in a production environment.
• Familiarity with MLOps (machine learning operations) principles, such as model versioning, CI/CD pipelines for ML, and model monitoring in production
5.Preferred Qualifications:
• Experience with reinforcement learning, unsupervised learning, or generative models (e.g., GANs).
• Knowledge of ethics in AI, such as mitigating bias and ensuring fairness in models.
Familiarity with NLP libraries like SpaCy, NLTK, Hugging Face Transformers, etc.
• Experience in building and deploying AI-powered products in a commercial setting.
• Knowledge of edge computing and deploying AI models on edge devices
6.Work Environment:
• Collaborative and fast-paced work environment.
• Opportunity to work with state-of-the-art technologies.
• Supportive and dynamic team culture
• The position may require collaborating across multiple teams, including product, engineering, and research groups, to develop and implement AI/ML solutions
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