Cloud With IOT

Location : Lisle

Salary : Full time : $130k

Exp : 8 to 15Years

Day 1 onsite : Onsite

We need team members who will actually wok on software designs

Job Description:

1. Expertise on Automotive OEM - Cloud, Connectivity, Telematics and IOT
2. Comprehensive architecture experience to transfer data from vehicles to leading cloud providers such as AWS, Azure, or Google Cloud Platform.
3. Experience in adeptness in cloud technologies that orchestrate the seamless integration of the proposed architecture with the chosen cloud provider's ecosystem. Whether leveraging AWS's IoT Core, Azure's IoT Hub, or Google Cloud IoT Core, Need to ensure streamlined integration process, allowing for seamless data flow from edge to cloud

Required Skills:

1. In-Vehicle Components:

• On-Board Diagnostics (OBD) System: Collects vehicle telemetry data such as engine performance, fuel consumption, and diagnostic trouble codes. Driver Monitoring System (DMS): Captures driver behavior data including steering patterns, acceleration, braking, and adherence to safety protocols. Edge Devices: Embedded systems within vehicles for data processing and communication.

1. Edge Connectivity:

• MQTT Protocol: Lightweight messaging protocol for efficient communication between edge devices and cloud infrastructure. IoT Gateway: Acts as a bridge between in-vehicle systems and the cloud, facilitating secure data transmission.

1. Cloud Infrastructure:

• Microservices Architecture: Modular architecture for scalability, flexibility, and ease of maintenance. Docker Containers: Containerized deployment of microservices for portability and consistency. Kubernetes: Container orchestration platform for automating deployment, scaling, and management of containerized applications.

1. Data Ingestion & Processing:

• MQTT Broker: Manages communication between edge devices and cloud services. Kafka Cluster: Event streaming platform for ingesting, processing, and distributing vast streams of data in real-time. Data Ingestion Service: Captures data streams from MQTT broker and forwards them to Kafka topics. Data Processing Microservices: Handle real-time and batch processing of incoming data streams, including normalization, validation, and aggregation.

1. Data Storage & Analysis:

• Distributed Database: Stores processed data for historical analysis and reporting. Analytics Microservices: Perform advanced analytics on stored data to derive actionable insights, such as driver behavior patterns, vehicle performance trends, and predictive maintenance.

1. Connectivity & Security:

• API Gateway: Exposes endpoints for external services and applications to interact with the system securely. Identity & Access Management (IAM): Enforces access controls and authentication mechanisms to safeguard sensitive data. Encryption: Secures data transmission and storage to protect against unauthorized access and data breaches

1. Monitoring & Management:

• Logging & Monitoring Services: Capture system metrics, logs, and events for performance monitoring and troubleshooting. Alerting Mechanisms: Notify administrators of critical events, anomalies, or system failures in real-time. Dashboarding Tools: Visualize system performance, data insights, and KPIs for stakeholders and decision-makers