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VC-backed company, mid-level SDE in metros with broad popular skills increases candidate competition.
Strong SRE and observability bias, but cloud and distributed systems skills are moderately transferable across industries.
Mandatory 5+ years plus specific cloud, Kubernetes, observability, and language requirements create strict screening.
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Design and develop a scalable observability platform integrated with AI agents for cloud-native and hybrid infrastructures.
Build and optimize large-scale, fault-tolerant analytics pipelines and distributed systems for processing high-velocity telemetry data.
Implement and fine-tune large language models (LLMs) and AI agent frameworks for automated insights and troubleshooting, collaborating with ML engineers on AI model deployment.
5+ years of experience building highly scalable systems.
Strong programming skills in Python and Golang; Rust experience is a plus.
Hands-on experience with cloud infrastructure (AWS, GCP, or Azure) and Kubernetes.
Bachelor's degree in Computer Science, Engineering, or related field.
Experienced in developing distributed systems and large-scale analytics pipelines with a focus on observability technologies (e.g., Prometheus, OpenTelemetry).
Proficient with LLMs, AI agents, and agent frameworks (e.g., langchain, autogen) to enhance automated troubleshooting capabilities.
Skilled at engineering real-time data processing and stream processing systems in a cloud-native environment.