About the job
Join blueoptima as a Machine Learning Data Engineer, where your expertise will drive the creation and management of vital data pipelines to interpret our distinctive GIS datasets. You will develop analyses that range from foundational statistics to advanced machine learning solutions.
Our innovative technology demands skilled engineers who are enthusiastic about problem-solving and ownership of the solutions they implement. This is an exciting opportunity to be part of a rapidly expanding company in its early stages, allowing you to craft your own success story.
Your Team
Our Data Engineering team accelerates value creation for stakeholders by delivering production-ready solutions and actionable insights. We enhance every aspect of blueoptima’s data, driving success through data-driven innovation.
You will collaborate closely with a diverse team of experts in Data Analytics, Machine Learning, Computer Vision, Artificial Intelligence, and Geospatial Analytics. The engineering team is primarily located in our Gurgaon office.
Key Responsibilities
- Utilizing your statistics and ML expertise to address real-world challenges with our collision risk analytics platform.
- Spotting new opportunities to automate processes and derive insights from diverse data sources.
- Keeping abreast of cutting-edge numerical and computer vision methodologies in the ML field.
- Implementing models on AWS infrastructure.
- Taking ownership of projects from prototype to automation, deployment, and maintenance.
- Establishing monitoring and data quality automation for production solutions.
- Mentoring team members in best practices for solution ownership and development.
- Effectively communicating complex ideas to client-facing teams and non-technical stakeholders.
- Designing and maintaining optimized, robust, and scalable systems.
- Identifying project requirements and critical paths to resolve challenges, along with providing clear timeline estimates.
- Employing data-driven strategies to validate assumptions and track progress on implemented changes.

