Who is AiDash?
AiDash is making critical infrastructure industries climate-resilient and sustainable with satellites and AI. Using our full-stack SaaS solutions, customers in electric, gas, and water utilities, transportation, and construction are transforming asset inspection and maintenance - and complying with biodiversity net gain mandates and carbon capture goals. Our customers deliver ROI in their first year of deployment with reduced costs, improved reliability, and achieved sustainability goals. Learn more at www.aidash.com.
What you will do?
- Design and develop robust backend systems supporting the ML lifecycle, including model training, deployment, and monitoring.
- Contribute to the architecture and build of scalable ML platforms, ensuring performance and efficiency.
- Build and govern the ML lifecycle (data pre-processing, experimentation, training, testing, deployment, monitoring, and maintenance)
- Build frameworks and tools to enable active learning.
- Build model testing tools and framework.
- Collaborate with cross-functional teams, including data scientists and product managers, to translate business needs into technical implementations.
- Maintain and improve existing ML systems, optimizing for scalability and reliability.
- Stay on top of the latest developments in ML platforms and backend technologies, applying best practices to our systems.
- Mentor junior engineers and contribute to team growth and skill development
What are we looking for?
- A minimum of 3 years of experience in Machine learning systems.
- Solid hands-on understanding of the ML lifecycle and distributed computing system
- Experience working in a product company, preferably in a role that involved developing ML-driven solutions.
- Proficiency in programming languages and tools relevant to ML engineering (e.g., Python, MLflow, Kubeflow).
- Excellent problem-solving skills, with the ability to innovate and think creatively.
- Strong communication skills and ability to work effectively in a team environment.
- Experience in building ML Platform and related tools such as Human in the loop.
- Experience in monitoring models in production setup.
- Experience in setting feedback loop for model improvement.
What other experience will make you a great candidate?
- Experience in leading large initiatives.
- Previous experience as a Data Scientist is good to have.
