Staff Data Engineer, ML

This is the platform seat behind Netradyne's fleet analytics: the ML and data infrastructure that the company's computer vision products depend on. You would take senior technical ownership of production ML pipelines, generative AI capabilities and real time streaming systems spanning cloud and edge, embedded in a team of machine learning engineers and data scientists. Concretely that means ETL and ELT on AWS, streaming from fleet devices through Kafka and Kinesis, LLM work including prompt engineering, fine tuning and retrieval augmented generation, and agentic systems that orchestrate retrieval and actions across ML services with observability and governance attached. MLOps is explicitly in scope: CI/CD for model training, automated testing, model versioning, monitoring and orchestration through Airflow or MLflow. The posting asks for 5 to 8 years, so despite the Staff title it is not a ten year role.

What the posting requires:
- 5 to 8 years of industry experience in data engineering, machine learning engineering or related roles.
- A B.Tech or equivalent qualification.
- Strong understanding of generative AI system design: prompt engineering, RAG architectures, vector search, and evaluation techniques for LLM applications in production.
- Experience with agentic AI patterns, including designing, orchestrating and monitoring autonomous or semi autonomous agents that coordinate tools, data sources and ML services with safety, observability and governance controls.
- Experience with workflow orchestration platforms such as Apache Airflow, Prefect or Dagster.
- Exposure to real time streaming and messaging systems such as Apache Kafka, Amazon Kinesis and Amazon SQS.
- Knowledge of containerisation and orchestration with Docker and Kubernetes or EKS.
- Proficiency with at least one Python web framework such as FastAPI, Django or Flask for data and model serving APIs.
- Experience with data visualisation and BI tools such as Tableau, Grafana or Plotly Dash.
- Familiarity with PyTorch, TensorFlow and Hugging Face Transformers.
- Background in large scale data processing and lakehouse or warehousing technologies such as Snowflake, Redshift, Spark or EMR, and Delta Lake.
- Experience with rapid prototyping tools such as Streamlit, Gradio or Dash for demonstrating ML solutions to stakeholders.

What the work actually looks like:
- Design, develop and deploy production ready solutions using generative AI, traditional ML, data science workflows and ETL or ELT pipelines on AWS and hybrid edge to cloud environments.
- Build and manage real time and batch streaming pipelines for high volume fleet device data.
- Implement MLOps practice: CI/CD for training, automated testing, model versioning, continuous monitoring and orchestration.
- Champion data quality and governance with data contracts, validation and SLA monitoring.
- Collaborate with product, data science, ML engineering and operations to put AI solutions into customer facing products.

Location and working pattern: Bangalore. This posting does not state the number of office days.

A note on the employer: Netradyne postings carry a recruitment fraud warning. Official communication comes only from netradyne.com or us-greenhouse-mail.io addresses, and the company never asks candidates for fees or sensitive data over unsecured channels.

Honest fit guidance: the Staff title sits on a 5 to 8 year range, so treat it as a senior individual contributor role rather than a principal one. The generative AI and agentic requirements are listed as mandatory, not preferred, which is a meaningful filter.