Description

As a Data Engineer specializing in ML Ops, you will design and maintain data infrastructure, ensuring seamless data flow from sensors to analytics. This role involves collaborating with various teams to optimize data processes and uphold data quality.

Requirements

• •3+ years of hands-on experience building production data pipelines in the cloud (AWS, GCP, or Azure)
• •Proficiency in Python, SQL, and at least one big-data framework
• •Familiarity with ML Ops tooling: DVC, MLflow, Kubeflow, or similar
• •Experience designing and operating data warehouses/data lakes (e.g., Redshift, Snowflake, BigQuery, Delta Lake)
• •Strong understanding of distributed systems, data serialization (Parquet, Avro), and batch vs. streaming paradigms
• •Excellent problem-solving skills and the ability to work in ambiguous, fast-paced environments
• •Background in robotics or sensor data (radar, LiDAR, camera pipelines)
• •Knowledge of real-time data processing and edge-computing constraints
• •Experience with infrastructure as code (Terraform, CloudFormation) and CI/CD for data workflows
• •Familiarity with Kubernetes and containerized deployments
• •Exposure to vision-language or action-planning ML models

Tasks

• •Build and operate data pipelines
• •Ingest, process, and transform multi-sensor telemetry
• •Design scalable storage solutions
• •Architect high-throughput, low-latency data lakes and warehouses
• •Enable ML Ops workflows
• •Integrate DVC or MLflow for model training automation
• •Track data and model lineage
• •Ensure data quality through validation and monitoring
• •Implement alerting for anomalies and schema changes
• •Collaborate cross-functionally with Embedded Systems and Robotics teams
• •Align on data schemas, APIs, and real-time requirements
• •Optimize performance of distributed processing and queries
• •Tune storage layouts for cost-efficiency and throughput
• •Document data schemas and pipeline architectures
• •Evangelize ML Ops practices within the team

Tools & Technologies

Benefits