Senior Platform Software Engineer

• Design, develop, test, and maintain software components for AI and cloud platform services.

• Implement backend services, APIs, integrations, automation, data-processing components, and operational tooling.

• Work with engineers and product managers to understand requirements and translate customer scenarios into software features.

• Contribute to rapid iteration by investigating customer feedback, troubleshooting issues, and improving product functionality.

• Write clean, maintainable, secure, and well-tested code following established engineering practices.

• Build unit, integration, and functional tests; participate in performance and resilience testing.

• Add logging, metrics, telemetry, health checks, and alerts to improve service observability.

• Help investigate, debug, and resolve service issues in development and production environments.

• Participate in code reviews, design discussions, documentation, and engineering-planning activities.

• Automate repeatable operational tasks, deployment workflows, configuration, and testing.

• Learn and apply OCI standards for reliability, security, availability, and operational readiness.

What You’ll Bring

• 3+ years of professional software-development experience, including experience delivering production software or services.

• Strong programming skills in one or more languages such as Java, Go, Python, C++, C#, or similar.

• Experience developing backend applications, distributed-system components, cloud-native services, or data-processing applications.

• Ability to independently deliver well-scoped features and components, with guidance on broader technical design and prioritization.

• Experience working collaboratively in an Agile or iterative software-development environment.

• Curiosity and willingness to learn rapidly from customers, product managers, and senior engineers.

• Exposure to one or more of the following areas:

• Data engineering or data platforms, including ingestion, processing, storage, or pipelines

• IoT, device connectivity, telemetry, or edge-to-cloud systems

• AI/ML platforms, model training, model serving, RAG, evaluation harnesses, AI agents, or MCP

• Familiarity with REST APIs, source control, code review, automated testing, CI/CD, containers, or cloud services.

• Bachelor’s degree in Computer Science, Engineering, or related field, or equivalent practical experience.

Responsibilities

Software Development

• Implement features and services from defined technical requirements and designs.

• Develop maintainable code, APIs, integrations, and automated tests.

• Debug defects and improve performance, reliability, and usability of owned components.

• Participate in code reviews and apply feedback to improve technical quality.

Customer-Focused Delivery

• Support the team in understanding customer workflows and technical requirements.

• Help turn customer feedback into actionable engineering tasks and product improvements.

• Contribute to fast, iterative delivery while maintaining quality and engineering discipline.

• Learn the domain and help identify opportunities to make solutions more reusable across customers.

Reliability & Operations

• Add monitoring, logging, and telemetry to owned components.

• Assist with service troubleshooting, incident investigation, and root-cause analysis.

• Follow operational procedures and contribute to runbooks and support documentation.

• Help improve automation for deployments, testing, and routine operational tasks.

Security & Collaboration

• Follow secure-development practices and OCI engineering standards.

• Work effectively with engineers, product managers, operations, security, and customer-facing teams.

• Communicate progress, risks, and technical questions clearly and promptly.

• Pursue ongoing learning in cloud, data, IoT, and AI technologies.

Preferred Qualifications

• Experience with OCI, AWS, Azure, Google Cloud, or another cloud platform.

• Experience with Kubernetes, Docker, microservices, distributed data systems, or event-driven architectures.

• Exposure to generative AI, RAG, AI agents, MCP, or enterprise AI integrations.

• Experience supporting enterprise applications, scientific/research workloads, IoT systems, or internal platform services.