Software Engineer
Role details
Job location
Tech stack
Job description
We're seeking a Software Engineer to help design, build and scale the underlying systems, APIs, and tooling that power how our engineers deploy, operate and observe software. You'll craft secure, reliable, and scalable software that streamline developer workflows and automate infrastructure capabilities., As a Software Engineer, you'll design and implement new platform components that enhance our core tools. You'll maintain and improve our infrastructure to ensure robust, scalable solutions. The role includes automating processes, enhancing platform functionality, and exploring new technologies to advance our platform. You'll also provide support for platform-related issues as needed.
Requirements
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5+ years of experience in software development, platform engineering, or DevOps roles, with knowelege on platform enablement and developer experience.
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Knowledge designing, building, and scaling Internal Developer Platforms (IDPs) or platform-as-a-service (PaaS) solutions.
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Expertise in cloud infrastructure (AWS, GCP, or Azure) and cloud-native technologies.
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Strong knowledge of Infrastructure as Code (IaC) tools such as Terraform.
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Proficient in Kubernetes architecture and ecosystem, including experience deploying and managing production-grade clusters.
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Strong coding/scripting ability in Go, Python, or TypeScript, particularly for building APIs, backend services, or CLIs.
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Hands-on experience with CI/CD systems (, GitHub Actions, ArgoCD, Flux) and GitOps workflows.
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Solid understanding of developer workflows.
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Knowledge integrating AI/ML tooling or APIs into platform capabilities or developer tooling.
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Expertise with observability tooling (, Datadog, Grafana, Prometheus) and debugging platform performance.
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Experience building platforms with security and compliance in mind (RBAC, IAM, policy-as-code, secrets management). Additional Desired skills
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Experience building self-service interfaces for developers (custom developer portals, Backstage, or Port).
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Experience with feature flagging, progressive delivery, and canary deployments.
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Knowledge of AI engineering workflows, including model deployment, data pipelines, or prompt engineering best practices.
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Exposure to LLMOps concepts, tools, IDE or infrastructure (, Cursor, LangChain, Weights & Biases, MLflow).
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Familiarity with event-driven or microservices architecture using Kafka, NATS, or similar tools.
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Background in platform product management thinking - ability to design platforms as internal products.
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Contributions to open source projects or active participation in platform engineering or AI tooling communities.
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Experience driving DevEx improvements through telemetry, feedback loops, and platform evangelism.