Netradyne
About this role
Netradyne uses computer vision and edge computing to score driver behaviour for commercial fleets, and says it has vision analysed more than 25 billion miles. This role builds the data platform underneath that. You design and operate high throughput streaming pipelines that carry device telemetry, build the data platform pieces that support machine learning workflows including feature generation, training and validation, and work directly with the Data Science and ML teams to get models into production. It is genuinely a backend job as much as a data job: the posting asks for backend components and services, containerised distributed systems, and production monitoring and troubleshooting. The stack is Python, AWS with Kinesis, SQS and Lambda, Airflow for orchestration, Docker and Kubernetes on EKS, and Jenkins for CI/CD. Two readers asked for backend and data roles specifically, and this is squarely one.
Who this is for
What the posting asks for: a Bachelor's or Master's degree in Computer Science or a related field, and 3 to 6 years of Python based data or backend engineering. Required alongside that are strong programming fundamentals in data structures, algorithms and object oriented design, experience with AWS services such as Kinesis, SQS or Lambda, experience building and managing data pipelines and workflows with Airflow DAGs, a good understanding of data modelling and both relational and NoSQL databases, experience with Docker and Kubernetes, hands on GitHub and CI/CD tooling such as Jenkins, and strong Linux fundamentals and debugging skills.
Preferred, not required: prior experience at a product or SaaS company, streaming and event driven architectures, exposure to NoSQL stores such as MongoDB or Cassandra, a basic understanding of ML pipelines, and familiarity with modern AI concepts such as RAG, AI agents and prompting.
The real day to day: building and maintaining scalable pipelines for real time and batch processing on Kinesis and SQS based systems, developing data platforms that support ML feature generation, training and validation, optimising pipeline performance and reliability at high volume, writing backend components and services for data processing, deploying and troubleshooting production data systems, working with distributed systems in Docker and Kubernetes on EKS, partnering with Data Science and ML teams to productionise AI solutions, and following engineering practices around code quality, testing and CI/CD. The posting also lists working with AI agents to solve data engineering problems.
Location and setup: Bangalore. This posting does not state office days, but Netradyne's other Bangalore roles specify on site, general shift, 5 days a week at HM Tech Park, Whitefield, so assume in office and confirm.
Honest fit guidance: strong choice if you want data engineering that stays close to production ML rather than to BI and reporting, and if you are comfortable that a good share of the week is backend service work and on call style troubleshooting. Netradyne has three separate openings on today's list across different teams, so pick the one that matches rather than applying to all three.
Preferred, not required: prior experience at a product or SaaS company, streaming and event driven architectures, exposure to NoSQL stores such as MongoDB or Cassandra, a basic understanding of ML pipelines, and familiarity with modern AI concepts such as RAG, AI agents and prompting.
The real day to day: building and maintaining scalable pipelines for real time and batch processing on Kinesis and SQS based systems, developing data platforms that support ML feature generation, training and validation, optimising pipeline performance and reliability at high volume, writing backend components and services for data processing, deploying and troubleshooting production data systems, working with distributed systems in Docker and Kubernetes on EKS, partnering with Data Science and ML teams to productionise AI solutions, and following engineering practices around code quality, testing and CI/CD. The posting also lists working with AI agents to solve data engineering problems.
Location and setup: Bangalore. This posting does not state office days, but Netradyne's other Bangalore roles specify on site, general shift, 5 days a week at HM Tech Park, Whitefield, so assume in office and confirm.
Honest fit guidance: strong choice if you want data engineering that stays close to production ML rather than to BI and reporting, and if you are comfortable that a good share of the week is backend service work and on call style troubleshooting. Netradyne has three separate openings on today's list across different teams, so pick the one that matches rather than applying to all three.
Apply on company site
Opens www.netradyne.com, the employer's own application page. Applying is always free.