Job description

Software Development: Design, develop, test, and maintain software, services, APIs, tools, and automation using languages such as Python and Go. Cloud & Platform Engineering: Build and improve cloud-native services and platform capabilities across production and non-production environments. Kubernetes: Deploy, operate, and troubleshoot applications and services running on Kubernetes. Develop automation that simplifies deployment and platform operations. Reliability: Apply reliability engineering practices to improve system availability, performance, scalability, and operational efficiency. Automation: Identify repetitive operational activities and develop software and automation to reduce manual effort and operational toil. Observability: Use logs, metrics, traces, dashboards, and alerts to understand system behavior, troubleshoot issues, and identify opportunities for improvement. Analytics: Analyze operational and application data to identify trends, anomalies, recurring issues, and performance bottlenecks. Develop dashboards and reporting that provide actionable insights. AI/ML Infrastructure: Support infrastructure and platform capabilities for AI/ML training, LLM inference, and GPU-based workloads. Develop automation to simplify deployment and operation of these environments. AI-Assisted Engineering: Explore and apply LLMs and AI technologies to improve software development, troubleshooting, analytics, automation, and operational workflows. Scale & Resilience: Participate in capacity planning, performance testing, scale testing, and disaster recovery exercises. Continuous Improvement: Identify opportunities to improve platform reliability, developer experience, automation, and operational processes. Documentation: Create and maintain technical documentation, operational procedures, troubleshooting guides, and runbooks. Collaboration: Work closely with software engineering, platform, SRE, QA, AI/ML, security, architecture, and program management teams. Software Engineering: Solid understanding of software engineering fundamentals and experience developing and maintaining production software, services, tools, or automation. Programming: Proficiency in Python and/or Go (Golang), with the ability to write clean, maintainable, and testable code. Kubernetes: Hands-on experience with Kubernetes and containers, including deploying and troubleshooting applications. Familiarity with Helm, Kustomize, or similar tools is preferred. Cloud: Experience with at least one major cloud platform such as AWS, Google Cloud, or Azure. Infrastructure as Code: Familiarity with Terraform, Ansible, or similar infrastructure automation technologies. Reliability Engineering: Understanding of reliability concepts such as monitoring, alerting, SLIs/SLOs, incident management, capacity planning, and automation. Observability: Experience with or exposure to technologies such as Prometheus, Grafana, Splunk, OpenTelemetry, or similar observability platforms. Analytics: Ability to analyze system and application data to identify trends and troubleshoot issues. Familiarity with SQL, Python-based data analysis, dashboards, or reporting tools is a plus. Distributed Systems: Working knowledge of distributed system concepts including availability, scalability, networking, fault tolerance, and performance. AI/ML: Familiarity with AI/ML concepts, LLMs, model inference, or GPU workloads is a plus. Prior AI/ML infrastructure experience is beneficial but not required. Problem Solving: Strong analytical and troubleshooting skills with an interest in solving problems through software and automation. Collaboration: Strong communication skills and the ability to work effectively within cross-functional engineering teams.

Requirements

Must-haves

Software Development: Design, develop, test, and maintain software, services, APIs, tools, and automation using languages such as Python and Go.
Cloud & Platform Engineering: Build and improve cloud-native services and platform capabilities across production and non-production environments.
Kubernetes: Deploy, operate, and troubleshoot applications and services running on Kubernetes.
Develop automation that simplifies deployment and platform operations.
Reliability: Apply reliability engineering practices to improve system availability, performance, scalability, and operational efficiency.
Automation: Identify repetitive operational activities and develop software and automation to reduce manual effort and operational toil.
Observability: Use logs, metrics, traces, dashboards, and alerts to understand system behavior, troubleshoot issues, and identify opportunities for improvement.
Analytics: Analyze operational and application data to identify trends, anomalies, recurring issues, and performance bottlenecks. Develop dashboards and reporting that provide actionable insights.
AI/ML Infrastructure: Support infrastructure and platform capabilities for AI/ML training, LLM inference, and GPU-based workloads. Develop automation to simplify deployment and operation of these environments.
AI-Assisted Engineering: Explore and apply LLMs and AI technologies to improve software development, troubleshooting, analytics, automation, and operational workflows.
Scale & Resilience: Participate in capacity planning, performance testing, scale testing, and disaster recovery exercises.
Continuous Improvement: Identify opportunities to improve platform reliability, developer experience, automation, and operational processes.
Documentation: Create and maintain technical documentation, operational procedures, troubleshooting guides, and runbooks.
Collaboration: Work closely with software engineering, platform, SRE, QA, AI/ML, security, architecture, and program management teams.
Software Engineering: Solid understanding of software engineering fundamentals and experience developing and maintaining production software, services, tools, or automation.
Programming: Proficiency in Python and/or Go (Golang), with the ability to write clean, maintainable, and testable code.
Cloud: Experience with at least one major cloud platform such as AWS, Google Cloud, or Azure.
Infrastructure as Code: Familiarity with Terraform, Ansible, or similar infrastructure automation technologies.
Reliability Engineering: Understanding of reliability concepts such as monitoring, alerting, SLIs/SLOs, incident management, capacity planning, and automation.
Observability: Experience with or exposure to technologies such as Prometheus, Grafana, Splunk, OpenTelemetry, or similar observability platforms.
Distributed Systems: Working knowledge of distributed system concepts including availability, scalability, networking, fault tolerance, and performance.

Nice-to-haves

Kubernetes: Hands-on experience with Kubernetes and containers, including deploying and troubleshooting applications. Familiarity with Helm, Kustomize, or similar tools is preferred.
Analytics: Ability to analyze system and application data to identify trends and troubleshoot issues. Familiarity with SQL, Python-based data analysis, dashboards, or reporting tools is a plus.
AI/ML: Familiarity with AI/ML concepts, LLMs, model inference, or GPU workloads is a plus. Prior AI/ML infrastructure experience is beneficial but not required.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

pythongokubernetesaimlllmgolangawsazuresql

Platform: apple
Effort: medium