Build performance profiles that classify workload types (OLTP, OLAP, mixed) and map them to optimal resource configurations.
Develop heuristics or ML models that recommend tuning actions (indexing, partitioning, memory settings) based on the profile.
Integrate profile generation and tuning recommendations into the agent workflow: branch → profile analysis → apply tuning.
Collaborate with engineering teams to embed performance profiles into Guepard’s branching and time-travel infrastructure.
Document profile definitions, tuning strategies, and profiling results for visibility and audit.
Primary responsibilities
Design and implement data collection pipelines to gather query-level and system metrics from multiple DB engines (Postgres, ClickHouse, MySQL, MongoDB).
Define feature representations for workloads and implement profiling logic to classify workload types and resource needs.
Research and implement ML models or rule-based heuristics to predict optimal tuning actions (index suggestions, partition strategies, memory/config tunings).
Integrate the profile generation and recommendation components into the existing Performance Optimization Agent workflow.
Write clear documentation of profile schemas, tuning rules/models, and experimental results for audit and operational use.
Deliverables & collaboration
Deliver a working Performance Profile component that can (a) ingest metrics from branches, (b) produce workload profiles, and (c) output tuning recommendations.
Provide evaluation reports comparing baseline vs. recommended configurations (latency, throughput, resource utilization) and include test cases.
Collaborate closely with backend and infra engineers to ensure safe application of tuning actions within branching/time-travel infrastructure and CI/CD constraints.
Implement monitoring/validation to detect regressions and provide rollback or safety checks when applying automated tunings.
How to apply
To apply, send your application to jobs@guepard.run with the subject line indicated below.
You can also apply via the online link: https://lnkd.in/d8wuxwT3