Senior Software Engineer – Application Reliability, Kubernetes, GCP, SQL

Jobtailor

California (MO)

On-site

USD 150,000 - 190,000

Full time

4 days ago

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Job summary

Jobtailor is seeking a senior reliability engineering leader to own AI-powered application reliability across user-facing features. You will define SLI/SLOs, build observability with Looker on BigQuery/BigTable, and drive blameless postmortems while working with cross-functional teams.

The role emphasizes hands-on GenAI workflows, agent engineering, and incident response, requiring deep GCP experience, complex SQL skills, and strong collaboration across engineering leadership.

Qualifications

10+ years of software engineering with reliability, observability, or production operations focus.
Strong Python development skills for tooling and agent-based systems.
Production GCP experience deploying on GKE (Kubernetes).
Deep SQL expertise with BigQuery, complex queries and design.
Experience designing/operating SLI/SLO frameworks and error budgets.
Hands-on Looker dashboards and LookML for observability.

Responsibilities

Own the reliability of AI-powered applications and user-facing features.
Define, implement, and enforce SLI, SLO, and error budgets for APIs and systems.
Build and maintain Looker dashboards on BigQuery and BigTable.
Lead incident response, root cause analysis, and blameless postmortems.
Develop agent evaluation harnesses for benchmarking and testing.
Write complex BigQuery SQL for usage trends and anomaly detection.

Skills

Python Development

BigQuery SQL Expertise

GCP Experience

Application-Level SLI/SLO Frameworks

Agent Evaluation Harnesses

Education

Bachelor's Degree in Computer Science

Master's Degree in Engineering

Tools

Looker

BigQuery

BigTable

GKE

Cloud Logging

Cloud Trace

Cloud Monitoring

LangGraph

Job description

Responsibilities

Own the reliability of AI-powered applications and features from the user’s perspective
Define, implement, and enforce feature-level SLIs, SLOs, and error budgets for APIs, RAG systems, AI agents, and user-facing applications
Build and maintain application observability systems using Looker dashboards on BigQuery and BigTable
Provide visibility into feature health, error patterns, and usage trends for developers, product managers, and leadership
Design and build LangGraph-based agents for automated issue identification and remediation
Implement anomaly detection, root cause diagnosis, auto-rollback, feature-flag kill switches, and self-healing workflows
Develop agent evaluation harnesses for benchmarking, multi-step workflow testing, non-deterministic outputs, and regression testing
Write complex BigQuery SQL for usage trend analysis, anomaly detection, and operational analytics
Design BigQuery table schemas optimized for observability and debugging
Analyze application usage trends and adoption metrics to identify reliability risks, capacity needs, and degraded user experiences
Partner with application development teams to embed deployment safety, structured logging, and distributed tracing practices
Lead application-level incident response, root cause analysis, and blameless postmortems
Build Python tooling and automation to reduce mean time to detect and resolve application-layer issues
Apply emerging AI techniques to improve platform reliability and developer productivity
Collaborate with application developers, data engineers, infrastructure SREs, security, compliance, product teams, and leadership

Requirements

10+ years of experience in software engineering with significant focus on reliability, observability, or production operations
Bachelor's or Master's Degree in Computer Science, Engineering, or a related technical discipline
Strong Python development skills, including production tooling, automation, and agent-based systems
Production GCP experience deploying and managing applications on GKE (Kubernetes)
Deep SQL expertise with BigQuery, including complex queries, window functions, schema design, and cost optimization
Hands-on experience with BigTable or an equivalent high-throughput operational data system
Experience designing and operating application-level SLI/SLO frameworks, burn-rate alerting, and error budget policies
Strong application-layer debugging skills, including distributed tracing, profiling, structured log analysis, and dependency mapping
Experience building agent evaluation harnesses
Familiarity with A2A protocols, streaming architectures, and event-driven systems
Experience with feature flags, canary deployments, progressive rollouts, and automated rollback
Experience with GCP observability services such as Cloud Logging, Cloud Trace, and Cloud Monitoring
Exposure to AIOps concepts including ML-driven anomaly detection, automated root cause analysis, and intelligent alerting
Experience driving reliability culture across engineering teams
Active engagement with the evolving AI ecosystem
Hands‑on GenAI application development experience with LangGraph, agent engineering, prompt design, and agentic workflows
Experience building Looker dashboards and LookML models for operational observability

Core Competencies

Demonstrates expertise in application reliability and observability, leveraging strong Python development skills and deep SQL knowledge with BigQuery. Capable of implementing SLI/SLO frameworks and driving a reliability culture across engineering teams.

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