Who is AiDash?
AiDash is an AI-first vertical SaaS company on a mission to transform operations, maintenance, and sustainability in industries with geographically distributed assets by using satellites and AI at scale. With access to a continual, near real-time stream of critical data, utilities, energy, mining, and other core industries can make more informed decisions and build optimized long-term plans, all while reducing costs, improving reliability, and achieving sustainability goals.
AiDash is backed by top-tier investors, including Benhamou Global Ventures, National Grid Partners, Edison International, Schneider Electric and Shell Ventures. To learn more about how AiDash is helping core industries become more resilient, efficient, and sustainable, visit www.aidash.com.
What you will do
- Understand the current Data Science stack at AiDash and implement the best software development practices to optimise them and bring them to production.
- Work with Product, Analytics, and Engineering teams to bring models, and new features to life.
- Implement end-to-end ML workflows in a cloud environment
- Work onMLOps and ML Engineering tools such as Kubernetes, Dockers, Pachyderm,Data Version Control (DVC), ML-Flow etc. to make AiDash’s existing Data Science Stack more robust.
- Creating a seamless Data layer/platform that can handshake with AiDash’s propriety models and automatemodel retraining and inference pipelines.
- Review code developed by other engineers and provided feedback to ensure best practices (e.g., style guidelines, checking code in, accuracy, testability, and efficiency).
What are we looking for?
- Bachelor’s Degree in Computer Science, Electrical Engineering, or similar technical field with 3 years of relevant experience.
- Extensive experience in writing computer programs in Python
- Experienced inat least any one Data Engineeringrelated systems like Kubernetes, Docker, AirFlow, Apache Spark etc.
- Experienced in end-to-end implementation of software workflows in a cloud environment.
What other experience will make you a great candidate?
- Practical experience in Statistical, Computer vision & Machine Learning models
- Experience in Computer Vision, Machine Learning and Deep Learning frameworks (Scikit Learn, PyTorch / TensorFlow, OpenCV )
