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 Satellite 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 investors, including Benhamou Global Ventures, National Grid Partners, Edison International and Shell Ventures.
Do visit our website” https://www.aidash.com/” to learn more about how AiDash is helping core industries become more resilient ,effective and sustainable.
AiDash is looking for Sr. Machine Learning Engineers who can work on ML infrastructure and platforms that make it easier to build, deploy, and monitor machine learning models.
What you will do ?
- Design and develop scalable training and inference platforms
- Develop and Integrate data pre and post processing workflows for ML models
- Develop model monitoring service on top of inference platform
- Develop platforms to grade models at scale
- Develop model experimentation tools
- Develop sampling strategy for assessing model performance
- Oversee the entire lifecycle (design, experimentation, development, deployment, monitoring, and maintenance)
- Create reusable workflows for the Data Science Models and integrate them with the production features while avoiding redundancies
- Deploy code to production and engage in code reviews
- Refactor service to improve code quality, runtime efficiency and resource optimization
- Develop automation / active Learning frameworks for retraining models
What are we looking for?
- Bachelor's / Master’s Degree in Computer Science, Mathematics & Computing, Electrical Engineering, or a related field with a min of 6+ years of experience.
- Experience with machine learning ecosystem.
- Rock solid experience in monitoring models and data in a production setup
- Experience in sampling strategies for different types of models and use cases
- Experience in grading models at scale
- Experience in designing and developing distributed training and inference platforms using distributed computing (PySpark, Kubeflow, Kubernetes)
- Extensive experience in writing computer programs in Python.
- Experience with Docker.
- Experience in developing tools to evaluate model performance (MLFlow,Tensorboard,Wandb)
- Experience in MLOps (AWS, GCP, Azure )
- Experience in handling large datasets for training (HDFS,Datalakes,NoSQL/SQL)
Why will you enjoy working with us?
1. You will work on the cutting-edge technologies and learn new things at a fast pace.
2. You will work and collaborate with many people and teams across the company
3. You will directly see how your work impacts our product and customers.
