Shell is seeking an AI Engineer to transform advanced AI concepts into scalable, reliable, and maintainable solutions that power real-world applications across their diverse line of business, bridging the gap between experimentation and execution.
Requirements
- Experience in TensorFlow, PyTorch, or Keras
- Hands-on expertise with MLflow for tracking experiments, managing model lifecycle, and deploying models at scale
- Experience using Optuna for hyperparameter optimization, including defining search spaces and integrating with training pipelines
- Proven examples of implementing experimental pipelines and prototyping AI applications
- A mathematical background covering some subset of linear algebra, probability, multivariate calculus, geometry, and/or numerical methods
- Hands-on code development such as Python, Java, MATLAB, R, and software development skills
- Experience in cloud infrastructure and architecture across platforms such as Azure and AWS. Comfortable working in Unix-based shell environments, including Bash.
Responsibilities
- Expect to be technical contributor on AI/ML projects, guiding architecture, implementation and deployment strategies
- Productize and deploy AI models, ensuring scalability, and reliability
- Design and implement robust data and model pipelines for model training, evaluation, and inference
- Establish metrics to monitor, optimize model performance along model life cycle
- Retrain and update model as needed based on feedback for end-users’ business teams
- Adapt novel AI algorithms and incorporate emerging AI technologies into our applications
- Implement and optimize data I/O pipelines and training/inference scripts
Other
- Must have legal authorization to work in the US on a full-time basis for anyone other than current employer
- Must have Master's or Ph.D in computer science, engineering, mathematics, theoretical science, statistics, or a related scientific discipline
- At least 3+ years of experience in AI related domains
- Experience as a technical contributor in an AI-focused project
- Awareness of lifecycle and data management
