Full Stack Cloud Engineer, Telemetry and Observability Platform

Our client is looking for a Full Stack Cloud Engineer to support the design, development, testing, and deployment of their Telemetry and Observability Platform, an enterprise platform that pulls together vehicle telemetry, diagnostic information, engineering artifacts, manufacturing evidence, service data, and software lifecycle signals to deliver actionable diagnostic and software health insights. You'll work within a cross-functional Agile team building cloud-native services, data integrations, user-facing capabilities, and platform observability, taking technical direction from senior engineers and architects while progressively owning well-defined platform components and features.

Responsibilities

Design, develop, test, and maintain platform components under the guidance of senior engineers

Build and integrate REST APIs, backend services, data-processing functions, and event-driven workflows

Develop integrations with approved vehicle, service, engineering, manufacturing, and software-factory data sources

Support the ingestion, normalization, validation, storage, and retrieval of telemetry and diagnostic information

Contribute to web-based interfaces and dashboards presenting diagnostic, operational, AI-performance, and platform-health information

Implement logging, metrics, distributed tracing, error handling, and health checks for platform services

Write unit, integration, API, and automated regression tests

Participate in code reviews and address feedback related to maintainability, performance, security, and coding standards

Support CI/CD pipelines, containerized deployments, configuration management, and cloud environment troubleshooting

Assist with integrating AI services through governed APIs and standardized interface contracts

Implement role-based access controls and follow enterprise security, privacy, data-retention, and auditability requirements

Create and maintain technical documentation, including API specifications, deployment instructions, support procedures, and design notes

Participate in backlog refinement, estimation, sprint planning, demonstrations, and retrospectives

Investigate defects and operational issues using logs, metrics, traces, and other platform evidence

Identify technical risks, dependencies, and blockers and communicate them promptly to the delivery team

Required qualifications

Bachelor's degree in Computer Science, Software Engineering, Information Technology, or a related discipline, or equivalent practical experience

Approximately 4-7 years of full stack software development experience

Programming experience in one or more relevant languages, such as Python, Java, JavaScript, or TypeScript

Hands-on experience with Angular, JavaScript, HTML, CSS, Java, Spring Boot, SQL, and Python

Extensive experience with GCP and AWS

Experience developing or consuming REST APIs and working with structured data formats such as JSON

Understanding of object-oriented programming, software design principles, and common application architecture patterns

Experience with relational databases, SQL, and basic data modeling

Experience using Git-based source control and participating in peer code reviews

Familiarity with automated unit and integration testing

Basic understanding of cloud computing, containerization, and CI/CD practices

Ability to diagnose software issues using application logs and debugging tools

Strong written and verbal communication skills

Ability to work collaboratively with product owners, architects, engineers, data specialists, testers, and operations teams

Nice to have

Experience with Google Cloud Platform services such as Google Kubernetes Engine, Pub/Sub, Cloud Storage, or Cloud SQL

Experience with Docker, Kubernetes, Terraform, or similar cloud-native technologies

Experience developing frontend applications using React and TypeScript

Familiarity with event-driven architecture, asynchronous processing, and data pipelines

Experience implementing platform observability using logs, metrics, traces, dashboards, and alerting

Familiarity with OpenTelemetry or comparable observability standards and tooling

Experience with API specifications and tools such as OpenAPI or Swagger

Familiarity with authentication, authorization, role-based access control, and secure coding practices

Experience integrating AI or machine-learning services through APIs

Familiarity with prompt management, AI response validation, confidence scoring, or human-feedback capture

Experience working with telemetry, automotive diagnostics, connected-vehicle data, manufacturing information, or engineering lifecycle systems

General knowledge of diagnostic concepts such as DTCs, CAN, UDS, or DoIP, beneficial but not required

Experience working in an Agile or Scrum delivery model

Understanding of agentic AI workflows, data pipeline build and processing, AI evaluation strategies using RAGAS, RAG and vectorization concepts, and LLM orchestration