AI Engineer
Introduction
An AI Engineer is responsible for designing and building applications and AI/ML models tailored to specific use cases such as predictive analytics, natural language processing, and computer vision. They collaborate with data, engineering, and software development teams to create scalable AI pipelines that can be integrated into existing systems.
Responsibilities
- Design and build applications and AI/ML models tailored to specific use cases.
- Create scalable AI pipelines that can be integrated into existing systems.
- Collaborate with data, engineering, and software development teams.
- Evaluate emerging AI trends, tools, and vendor solutions against business use cases.
- Run proof-of-concepts (PoCs) to test feasibility of new ideas.
Requirements
Required Skills:
- Strong Python skills, familiarity with Java/C++/Go for production environments.
- Object-oriented programming and design patterns.
- Unit testing, CI/CD, Git, containerization (Docker).
- Data pipelines (Airflow, Prefect, or cloud-native equivalents).
- Model deployment (REST APIs, gRPC, serverless), monitoring, and versioning.
- Experience with AWS/Azure/Google Cloud Platform/OCI AI services.
- Cloud-native training/inference environments (SageMaker, Vertex AI, Azure ML).
- Knowledge of Kubernetes for scalable inference.
Preferred Skills
- Experience with Artificial Intelligence, Cloud Computing, Machine Learning (ML), and Natural Language Processing.
- Proficiency in Amazon Web Services, Microsoft Azure, Google Cloud Platform, and Oracle Cloud Infrastructure.
- Knowledge of Continuous Integration, Continuous Delivery, and Data Engineering.
- Experience with Docker, Git, and Software Development.
