Job Summary (List Format):
- Architect and deliver end-to-end AI systems and frameworks, focusing on balancing speed, reliability, and maintainability.
- Design and implement complex cloud architecture for large-scale AI platforms, including compute, storage, security, deployment, and monitoring.
- Assist in building scalable data pipelines to support advanced AI systems and enable continual model lifecycle improvements.
- Define and develop low-latency APIs/services to operationalize AI models and integrate them into enterprise applications.
- Develop monitoring and reliability solutions to ensure consistent performance, accuracy, and stability of AI models in production.
- Participate in agile development practices, ensure peer code reviews, and conduct code reviews for others.
- Create and maintain thorough documentation in line with team procedures and corporate policies.
- Build AI solutions using cloud platforms (e.g., Azure AI services), with experience in highly regulated environments preferred.
- Productionize AI models, including implementation of scalable pipelines and robust monitoring systems.
- Develop deep learning models using modern frameworks such as TensorFlow, PyTorch, or MLX.
- Apply software design patterns, microservices, distributed systems, and container orchestration.
- Implement ethical AI practices, including explainability, fairness, and bias mitigation.
- Implement advanced LLM (Large Language Model) solutions (e.g., retrieval-augmented generation, structured reasoning), and set team best practices.
- Debug and optimize AI systems for performance and reliability.
- Communicate technical concepts and tradeoffs effectively to non-technical stakeholders.
- Mentor and share knowledge to raise team capability, demonstrating leadership and collaboration skills.
- Required skills: Azure AI, Python.