Transforming prototype AI systems into scalable, production-ready software solutions and engineering robust applications that deliver real-world value to customers.
Requirements
- Solid understanding of deep learning frameworks such as TensorFlow, PyTorch, JAX, or ONNX.
- Proficiency in Python and experience with RESTful APIs, microservices, and cloud platforms (AWS, Azure, GCP).
- Experience with Git, Docker, Kubernetes, and CI/CD tools.
- Familiarity with MLOps tools and practices (e.g., MLflow, Airflow).
- Experience with model optimization techniques (e.g., quantization, pruning, distillation).
- Knowledge of data privacy, compliance, and security in AI deployments.
- Exposure to real-time inference systems and large-scale data pipelines.
Responsibilities
- Translate AI prototypes into scalable, maintainable software systems.
- Develop APIs, microservices, and backend infrastructure to support AI functionality.
- Collaborate with Data Scientists to integrate deep learning models into production environments.
- Optimize model performance for inference speed, memory usage, and scalability.
- Containerize and deploy applications using Docker and Kubernetes across cloud or on-premises platforms.
- Implement CI/CD pipelines and automated testing frameworks.
- Monitor deployed systems and troubleshoot production issues.
Other
- 3+ years of experience in software development, with a strong focus on AI/ML systems.
- Prior experience in customer-facing roles or product development.
