Platform Engineer (Cloud Infrastructure (SMTS))

Join our Platform Engineering team as a Senior Member of Technical Staff (SMTS) focused on AI/ML software engineering. You will design, build, and operate platform services and infrastructure automation, embedding AI capabilities directly into the core platform software. Your success will be measured by how effectively you integrate AI, LLMs, and autonomous agents into our multi-cloud platform services to improve system reliability, reduce operational toil, and elevate the developer experience.

• Design, build, and operate platform services and infrastructure automation in Go and Python, embedding AI capabilities directly into the core platform software.
• Architect and implement intelligent, closed-loop automation systems (AIOps) that leverage LLMs and autonomous agents to detect anomalies, perform root-cause analysis, and execute self-healing remediation playbooks.
• Partner with SRE, security, and platform specialists to identify highly repetitive operational work and build agentic solutions that delegate that toil to AI.
• Strong understanding of core AI and ML concepts applied practically to software engineering, including LLM context window optimization, embedding models, semantic search, vector databases, and prompt engineering/tuning
• Familiarity with continuous deployment and infrastructure-as-code concepts (GitOps with Flux/Argo CD, Pulumi, or Terraform)
• 5+ years of professional experience in software engineering, platform engineering, or DevOps, with a recent, heavy focus on building and implementing AI solutions
• Experience building with agentic frameworks and LLM orchestration tooling to execute multi-step, autonomous tasks
• Solid fundamental knowledge of cloud-native infrastructure, with hands-on experience in Kubernetes and multi-cloud environments (AWS, Azure, GCP, or OCI)
• Good programming skills in Golang and Python, with the ability to build production-grade backend services, APIs, and microservices
• Strong communication and collaboration skills, with a passion for teaching, raising the team’s AI literacy, and evangelizing AI solutions across engineering boundaries
• Demonstrated agentic and automation mindset — you have a proven track record of using AI to automate complex workflows and can speak deeply on how you design AI systems to handle edge cases, tool-calling errors, and non-deterministic outputs
• Hands-on experience building custom extensions, plugins, or Model Context Protocol (MCP) servers for agentic developer tools like Claude Code or GitHub Copilot
• Experience applying AI specifically to observability data (parsing logs, analyzing metrics, or correlating distributed traces) for predictive scaling or automated alerting
• Deep experience working with vector databases (e.g., Pinecone, Qdrant, Milvus, pgvector) inside platform applications
• Experience with internal developer platforms (IDPs), platform APIs, or building developer experience (DevEx) tooling
• Experience operating AI-driven tools within compliance-driven environments (FedRAMP, SOC 2), ensuring strong data privacy boundaries, LLM guardrails, and secure handling of sensitive cloud credentials
• Contributions to open-source projects is a plus