AI/ML Engineer
6 days ago
We are seeking a highly skilled AI/ML Engineer to join our team at Onelab Ventures. As a key member of our team, you will be responsible for designing, developing, and deploying cutting-edge artificial intelligence and machine learning solutions.
Key Responsibilities:- Design and develop machine learning models and algorithms to solve real-world problems and improve business processes.
- Work with large complex datasets by cleaning, organizing, and preparing the data for training and validation of machine learning models.
- Train machine learning models using frameworks like TensorFlow, PyTorch, or Scikit-learn and fine-tune models to improve accuracy and performance.
- Deploy models into production environments using platforms such as AWS SageMaker, Azure ML, or Google AI and monitor their performance in real-time applications.
- Stay updated on the latest research in machine learning and AI and apply relevant findings to improve model accuracy, efficiency, and scalability.
- Work closely with data scientists, data engineers, and software developers to integrate AI models into existing systems and workflows.
- Use evaluation metrics such as accuracy, precision, recall, F1 score, and ROCAUC to assess model performance and identify opportunities for improvement.
- Automate data workflows and model training pipelines using cloud services like AWS, Azure, or GCP and tools such as Docker and Kubernetes.
- Maintain clear and concise documentation of the models, pipelines, and processes developed, ensuring they can be understood and maintained by the team.
- Experience: 2-5 years of experience in AI/ML development, with a focus on building, training, and deploying machine learning models in a production environment.
- Programming Skills: Proficiency in programming languages such as Python, R, or Java. Experience with machine learning libraries like TensorFlow, PyTorch, or Scikit-learn.
- Data Management: Strong understanding of databases, data structures, and ETL processes. Experience working with SQL, NoSQL, or other databases.
- Cloud Experience: Hands-on experience with cloud platforms like AWS, Azure, or Google Cloud, particularly in deploying machine learning models and managing infrastructure.
- Containerization: Knowledge of Docker and Kubernetes for containerizing machine learning applications and managing large-scale deployments.
- Modeling: Solid understanding of supervised and unsupervised learning techniques, deep learning, natural language processing (NLP), and reinforcement learning.
- Problem-Solving: Strong analytical skills and the ability to think critically about how to solve complex problems with machine learning solutions.
- Communication: Excellent communication and collaboration skills to work with cross-functional teams and convey technical information to non-technical stakeholders.
- Education: Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or a related field. A Ph.D. in a relevant area is a plus.
- Certifications: Certifications in AI/ML, such as AWS Certified Machine Learning Specialty, Microsoft Certified Azure AI Engineer, or Google Professional ML Engineer, are highly desirable.
- Tools and Frameworks: Experience with tools like Jupyter, Git, and MLflow for model versioning and experimentation tracking.
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