About the Job
We are seeking a highly skilled Senior AI/ML Cloud Engineer to join our innovative team. In this role, you will be responsible for designing, developing, and implementing cutting-edge AI solutions across multiple cloud platforms. You will work on projects that leverage advanced machine learning, deep learning, and large language models to solve complex business problems.
Education Requirements
- Bachelor’s degree in a related discipline.
Skills Requirements
- Experience with Python programming language. Experience with transforming legacy code (e.g., Java, .Net) into cloud-native microservices.
- 2 years of experience of managing AI services within one cloud platform (e.g. GCP, Azure, AWS).
- Experience with container services and orchestration (e.g. GKE, EKS, AKS, ECS).
- Experience in common machine learning, deep learning, and LLM frameworks, such as TensorFlow, PyTorch, scikit-learn, Hugging Face Transformers, LangChain, LangGraph.
Experience Requirements
- In-depth knowledge of data services across major cloud platforms (e.g. GCP, AWS, Azure).
- Professional certifications focus on AI/ML from GCP, AWS, and/or Azure.
- Experience with real-time machine learning and streaming data processing.
Role & Responsibilities
- Design and develop AI and machine learning solutions using cloud-based managed AI services.
- Implement and manage robust monitoring systems for AI/ML models in production environments, ensuring continuous performance tracking, anomaly detection, and model drift analysis; collaborate with cross-functional teams to deploy model updates, maintain version control, and optimize model efficiency over time.
- Containerize AI applications and deploy them using cloud orchestration services.
- Collaborate with data engineers and data scientists to build end-to-end AI pipelines.
- Implement MLOps practices to streamline the development, deployment, and monitoring of AI models.
- Use Infrastructure as Code (IaC) to manage and version cloud resources for AI projects.
- Ensure clear and accessible knowledge transfer to internal teams and create knowledge-sharing resources to ensure smooth transitions during model handoffs and system updates.
- Stay up-to-date with the latest advancements in AI and machine learning technologies.
- Contribute to the development of best practices and standards for AI engineering within the organization.