Software Engineer

Drive the engineering of connected vehicle data, analytics, and AI/ML capabilities, taking use cases from vehicle data and experimentation through production deployment and continuous improvement.

Connected Vehicle Data & Analytics: Build analytics capabilities using vehicle telemetry, diagnostics, usage, and operational data.

AI/ML Engineering: Develop and productionize ML models for predictive analytics, vehicle health, battery intelligence, and vehicle/customer insights. Build feature, training, inference, and model-serving pipelines.

MLOps & Production AI: Establish the ML lifecycle from data and training through validation, deployment, monitoring, and retraining. Implement model/version management, drift detection, and reusable ML services.

AI Platform & GenAI: Enable relevant GenAI/LLM, RAG, and AI-Agent use cases and establish reusable AI engineering patterns.

Data & AI Governance: Ensure data quality, lineage, security, observability, and appropriate AI governance.

Technical Leadership: Define data/AI engineering standards, evaluate technologies, work with vehicle/platform/cloud teams, and guide implementation partners.

Strong Python and SQL; PyTorch, TensorFlow and/or Scikit-learn.

Spark/PySpark and streaming technologies such as Kafka, MQTT or equivalent.

AWS, Azure or GCP; MLOps tools such as MLflow, Kubeflow, SageMaker, Vertex AI.

Docker, Kubernetes, CI/CD, large-scale data processing.

Strong understanding of ML model deployment, monitoring, and the end-to-end ML lifecycle.

Connected Vehicle, Automotive, IoT, or Mobility experience; Vehicle Telemetry / Time-Series Data.

Predictive Maintenance, Vehicle Health, Battery Analytics, EV Data, or Digital Twin.

Real-time Streaming Analytics and Large-scale Fleet Data.

GenAI / LLM / RAG / AI Agents and experience taking AI solutions from PoC to Production.

Experience working with Engineering Partners / Vendors.