Senior Core Infrastructure Engineer
Description
At Oracle Cloud Infrastructure (OCI), we build the future of the cloud for Enterprises as a diverse team of fellow creators and inventors. We act with the speed and attitude of a start-up, with the scale and customer-focus of the leading enterprise software company in the world. Compute is one of the core organisations within OCI. We are responsible for providing Compute power i.e. VMs, BMs and GPUs. Cloud pretty much cannot exist without our org. We develop and operate multiple services (Provisioning, Monitoring, Repair, Control Plane, Data Plane, Re-imaging etc) behind the scenes which work like magic for our customers.
We’re looking for hands-on engineers with expertise and passion in solving complex problems in distributed large-scale systems, virtualised cloud infrastructure, and highly available services. You will help build automation that diagnoses infrastructure issues, orchestrates recovery workflows, and reduces manual operational effort across a large-scale compute fleet. You should be expert in Java, Linux, Python, Terraform, CI/CD tools, networking, microservices architecture and system engineering. We are looking for someone who values simplicity and scale, works comfortably in a collaborative, agile environment, and is excited to learn.
Key Responsibilities
Own and build a collection of services and/or technology areas as a senior software developer. Understand the end-to-end architecture, call flows, configurations, technical dependencies, and overall behavioral characteristics of production services. Responsible for the design, development and maintenance of the mission critical stack, with focus on security, resiliency, scale, and performance. Authority for end-to-end performance and operability. Define and develop improvements in service architecture. Articulate technical characteristics of services and technology areas. Engineer and add advanced capabilities to the Oracle Cloud service portfolio. Understand and communicate the scale, capacity, security, performance attributes, and requirements of the service and technology stack.
Demonstrate clear understanding of automation and orchestration principles. Design and develop reusable diagnostic and remediation workflows, translating operational runbooks into reliable, testable automation. Integrate with monitoring, incident management, and infrastructure services to support end-to-end issue resolution. Build safeguards into automated actions, including validation, retries, recovery, and escalation to engineers when needed.
Act as ultimate escalation point for complex or critical issues. Utilize a deep understanding of services and their dependencies required to troubleshoot issues and define mitigations. Use production telemetry and incident patterns to identify automation opportunities and measure improvements in resolution time, reliability, and operational effort. Understand and explain the impact of product architecture decisions on distributed systems. Demonstrate professional curiosity and a desire to develop a deep understanding of services and technologies.
- Bachelors/Masters in Computer science and Engineering or related engineering fields
- 3+ years of experience developing and operating large scale, highly available distributed systems.
- Experience in System Design, Platform & reliability engineering, ML engineering, data engineering, or AIOps-oriented roles.
- Strong hands-on experience building ML or LLM-based systems using Python,Java, PyTorch, TensorFlow, or modern LLM frameworks. Hands-on experience with at least one of the following tools: Claude Code, Codex, Github Copilot
- Deep understanding of observability data, including logs, metrics, and traces.
- Experience with observability platforms such as Datadog, Splunk, Prometheus, Grafana, or ELK.
- Experience designing and deploying RAG pipelines, embeddings, intent models, or operational chatbots.
- Strong experience architecting streaming or event-driven systems such as Kafka, Kinesis, or Pub/Sub.
- Familiarity with cloud-native systems, Kubernetes, microservices, and modern deployment patterns.
- Excellent problem-solving skills with the ability to translate operational challenges into ML-based or automation-based solutions.
- Ability to collaborate effectively across SRE, platform, service management, and engineering teams.
- Excellent problem solving, critical thinking, and communication skills
Skills
- Agile
- Automation
- CI/CD
- Claude Code
- Cloud
- Cloud Native
- Data Engineering
- Datadog
- Distributed Systems
- ELK
- Embeddings
- Event Driven Architecture
- GitHub
- Github Copilot
- Grafana
- Java
- Kafka
- Kinesis
- Kubernetes
- Linux
- LLM
- Machine Learning
- Microservices
- Networking
- Observability
- Oracle
- Prometheus
- Python
- PyTorch
- Splunk
- SRE
- TensorFlow
- Terraform
