AI / ML Engineer
We are seeking a dedicated ML/AI engineer to join our data analytics team. You will work as part of a diverse team of data experts, from data engineer, analytics engineer to business data analysts and to solve real-world problems and change the future by developing and applying new artificial intelligence models and algorithms.
As an artificial intelligence engineer, you will be expected to have a keen interest in artificial intelligence, machine learning, and staying updated with the latest developments in a rapidly changing field. Your duties will include collecting and analyzing data sets to identify patterns and develop predictive algorithmic models.
AI/ML Ops Establishment:
- Lead the implementation of AI/ML ops processes and infrastructure, with a focus on Databricks and Azure.
- Design and implement continuous integration/delivery (CI/CD) pipelines for seamless model deployment and monitoring.
- Implement and manage version control systems (e.g., Git) for ML/AI codebase.
- Optimize model performance through parameter tuning, ensemble methods, and regularization techniques.
LLMs and Deep Learning:
- Specialize in developing and deploying Large Language Models (LLMs) for natural language processing tasks.
- Implement and fine-tune deep learning models, including neural networks and recurrent neural networks (RNNs), for complex business use cases.
- Apply transfer learning techniques to leverage pre-trained models and adapt them to specific applications.
- Expertise in leveraging Databricks for ML/AI workflows, including feature engineering, model training, and hyperparameter optimization.
- Proficient in PyTorch or TensorFlow for developing, training, and deploying deep learning models.
- Familiarity with containerization tools (e.g., Docker) and orchestration frameworks (e.g., Kubernetes) for scalable and efficient model deployment.
Collaboration and Communication:
- Collaborate closely with data scientists, engineers, and cross-functional teams to understand business requirements and deliver advanced AI solutions.
Effectively communicate complex technical concepts to non-technical stakeholders.
Documentation and Reporting:
- Create and maintain comprehensive documentation for AI/ML models, algorithms, and workflows.
- Generate detailed reports on the performance, interpretability, and explainability of deployed models.
- Master's or PhD in Computer Science, Machine Learning, or a related field.
Strong academic background with coursework and projects demonstrating expertise in AI/ML ops, LLMs, and deep learning.
- In-depth knowledge of Databricks MLflow and Azure Machine Learning for end-to-end ML/AI lifecycle management.
- Hands-on experience with transformer-based models such as BERT, GPT, or XLNet.
- Expertise in deploying models at scale using cloud-based services.
- Proficiency in implementing and fine-tuning deep learning models with state-of-the-art frameworks.
Strong programming skills in Python, R, or Scala.