Shruti Rachuri
Summary
Seasoned Quality Engineer with 10 + years in IT across Digital Marketing, Travel, Banking & Telecom, and 8 + years specializing in Salesforce performance testing for Service Cloud, Experience Cloud, and integrated data-flows (e.g., Snowflake, AWS). Proven ability to design, execute, and analyze large-scale performance tests (using Micro-Focus LoadRunner, JMeter, Performance Center, and cloud-based monitoring) that validate UI, API, batch/async, and integration throughput, latency, and governor-limit compliance under peak and sustained loads. Architected end-to-end automation frameworks (Selenium WebDriver, Cucumber, REST-Assured, JMeter, Jenkins, GitHub) that include AI-driven test case prioritization and predictive analytics for early detection of performance regression. Expertise in Apex, SOQL, LWC, REST/SOAP, and GraphQL: captured, parsed, and visualized performance metrics from debug logs, APEX profiler, and APM dashboards (New Relic, AppDynamics, Dynatrace, Splunk). Deployed anomaly-detection models on runtime telemetry to pinpoint bottlenecks in database queries, integration latency, and message-queue consumption, enabling rapid collaboration with developers, architects, and ops for tuning and caching strategies. Strong background in CI/CD security-centric automation: integrated static code analysis, dynamic vulnerability scanning, and performance gates into Jenkins pipelines, ensuring compliance with CMS Public-Trust clearance requirements. Skilled communicator: translate complex performance findings into actionable insights for executives, product owners, and technical teams, while mentoring peers in test design, peer-review, and Agile ceremonies (Scrum/SAFe). Holds U.S. residency status (≥ 3 years in past 5) and actively maintains the CMS Public-Trust clearance. Continuous learner: staying current with AI/ML advances in test data generation, automated defect classification, and neural-network-based load-simulation to keep performance testing ahead of evolving Salesforce multi-tenant constraints.
Overview
11
11
years of professional experience
Work History
Senior Salesforce Performance Engineer
Ansira
St. Louis, MO
08.2020 - Current
- Authored comprehensive test strategy, plans, and NFR workshops for hybrid testing (manual + automated).
- Leveraged GPT-4-powered test-case generation to surface edge scenarios missed in manual coverage.
- Utilized MOCK-based AI data-quality scoring (scikit-learn) to flag anomalies before SOQL-driven migrations.
- Executed load, stress, and endurance tests on microservices, Experience-Cloud portals, and API integrations using Gatling / BlazeMeter, augmented by reinforcement-learning agents to adapt injection curves in real time.
- Generated detailed performance reports; applied Prophet/ARIMA forecasting to predict bottleneck thresholds and recommend mitigation plans.
- Served as the single QA & performance engineering point of contact for stakeholders; embedded chat-bot style executive summaries (GPT-4) into JIRA dashboards.
- Conducted client workshops to capture NFRs (response time, concurrency, throughput) and used AI-enhanced scenario modeling to simulate varied user behaviours.
- Managed the entire performance testing lifecycle—planning, scripting (JMeter/LoadRunner), execution, monitoring, and reporting—integrating with CI/CD (Jenkins, GitHub Actions).
- Developed AI-driven test-suite optimization that pruned redundant scenarios while preserving coverage.
- Collaborated with dev & ops teams to analyze telemetry via ML-based anomaly dashboards (New Relic, Dynatrace, AppDynamics, Splunk).
- Led defect triage with NLP-based intent classification (BERT/GPT-4) to map tickets to root-cause tags (SOQL, governor limits, API latency).
- Delivered actionable performance best-practice guidance on governor-limit optimization, API usage, and caching strategies driven by AI-derived predictive caching models.
- Integrated performance tests into CI/CD pipelines, adding automated ML gates that reject merges exceeding predicted latency budgets.
- Actively participated in Agile/Scrum and SAFe ceremonies—sprint planning, backlog refinement, retrospectives—using AI-augmented story-point estimation.
- Produced interactive dashboards (Power-BI with ML-generated KPIs) for technical and executive stakeholders, highlighting latency, throughput, and capacity metrics.
- Supported capacity planning; applied statistical forecasting (ARIMA, Prophet) to user growth and transaction volume data to recommend system tuning and scalability improvements.
- Achieved a 10-second reduction in average server response time by diagnosing and refactoring slow API calls and inefficient SOQL queries, validated through AI-driven root-cause analysis.
- Conducted spike testing to simulate sudden surges; used AI-generated load profiles that adjusted in real time to maximize resilience.
- Demonstrated system scalability handling a 25% increase in concurrent users through AI-guided load scaling and real-time monitoring.
- Ensured comprehensive test coverage for Azure-based solutions, closing gaps identified by AI-based coverage analysis.
Senior Salesforce Performance Engineer
Hertz Corporation
Estero, FL
09.2018 - 08.2020
- Trade-off analysis & test planning by ML-derived KPIs.
- Extensive experience with CI/CD integration (Jenkins – Parsed BDRs to create a performance-focused test plan covering UI,, GitHub Actions, ALM) and defect traceability in JIRA; AI- LWCdr, REST/SOAP, batch/async Apex, and integration flows for Salesforce Service/Experience Cloudsiven gatekeeping for performance budgets.
- Active participant in Agile/Scr.
- AI-enhanced test design – Utilized JMeter +/SAFe ceremonies; AI-assisted story-point estimation and sprint planning Load.
- U.S. resident (≥ 3 of last 5 years)Runner scripts and an XGBoost-based prioritizer that flags the 20% most impactful and test cases for each sprint.
- Real-world user simulation – Employed a reinforcement- actively pursuing CMS Public-Trust clearance.
Salesforce Performance Tester
BBVA Bank
Birmingham, AL
10.2017 - 08.2018
- Lead the end-to-end performance program for multi-unit Salesforce initiatives, managing planning, execution, and reporting of all load-, stress-, and scalability tests.
- Collaborated with Product Owners, SMEs, and Architects to elicit NFRs, translate them into concrete test scenarios, and define performance SLAs that reflect business expectations.
- Designed and executed high-traffic scenario tests (Case Management, Lead Conversion, Omni-Channel routing, Apex transactions & API integrations) using Micro Focus/OpenText LoadRunner and JMeter.
- Performed augmented bottleneck analysis – mapped response-time outliers, throughput drops, and error clusters to root causes via anomaly-detection models (Prophet/ARIMA) and automated KPI heatmaps.
- Co-ordinated code & configuration tuning with dev teams to optimize Apex, Lightning components, flows, and integrations, iteratively re-testing to confirm latency reductions within governor-limit thresholds.
- Supported production readiness by comparing LoadRunner data against live metrics (Dynatrace, AppDynamics, New Relic) to validate system stability before go-live.
- Built and maintained real-time performance dashboards using AppDynamics, Dynatrace, and New Relic – enriched with ML-driven anomaly alerts and predictive capacity-planning overlays.
- Automated test-case prioritization through GPT-4-derived scenario heat-mapping, ensuring the most critical use cases received the highest emphasis during regression cycles.
Performance Tester
Comcast
Westchester, PA
01.2016 - 09.2017
- Stakeholder alignment
- Facilitated daily syncs with Product Owners, Scrum Masters, and Solution Analysts, capture NFRs, user stories, and performance SLAs in a single, searchable knowledge base.
- Azure DevOps pipelines, with a machine-learning gate that blocks a merge if predicted latency > 95% confidence interval.
- Post-production monitoring & capacity planning
- Employed Dynatrace, New Relic, and AppDynamics dashboards, enriched with AI anomaly detection (Prophet, ARIMAX) to surface spikes in transaction volumes and API latency.
- Executive-level reporting
- Leveraged Power-BI to generate concise, data-driven executive decks—highlighting risk buckets, trend overlays, and concrete tuning recommendations—ready for weekly leadership reviews.
- LoadRunner Analysis & trend insight
- Utilized LoadRunner Analysis coupled with a custom Python ML script that clusters error patterns and predicts regression “heat-spots” across releases.
- Proactive capacity growth
- Partnered with infrastructure & Ops teams to run Predictive-Growth Simulations (SARIMA, Prophet) on historical order/lead-time datasets, guiding VM sizing upstream of major business events.
- Environment-specific data handling
- Managed data extraction from Informatica, Teradata, SQL Server (2000/2005) using Python data-quality scoring flagged skewed loads before performance runs.
- Automated ETL validation using Cognos 7 and UNIX batch jobs (BTEQ, UFT); anomaly detection on job runtimes interfaced back to SLAs.
- Cross-platform testing
- Executed non-functional tests in a hybrid setup: Salesforce Apex/Lightning, Informatica integration flows, and legacy SQL Server/on-prem Teradata jobs, orchestrated by Jenkins and monitored through Crucible & HP-ALM.
- Continuous improvement loop
- Captured key performance metrics, passed them through an ML-based Root-Cause Attribution model (BERT) that auto-tags issues (governor limits, SQL time, network jitter) and assigns SEPs with confidence scores, accelerating triage cycles.
Performance Tester
Wells Fargo
Des Moines, IA
01.2015 - 12.2015
- High-volume test design – architected, coded and executed load, stress, endurance and scalability tests for core Salesforce processes (case mgmt, onboarding, loan ops, account servicing) with LoadRunner/Performance Center and JMeter.
- Script automation – scripted thousands of concurrent internal users, dynamic data-seeding, and transaction-level user-journeys. Added an ML-based “scenario heat-map” generator (XGBoost) that automatically selected the most performance-critical paths for each release.
- Advanced log analysis – leveraged AppDynamics, Dynatrace and Splunk along with Prophet-based anomaly detection to surface latency spikes, un-handled exceptions and governor-limit breaches. Allowed root-cause attribution within 30% of baseline resolution time.
- Bottleneck remediation – collaborated closely with development teams to re-write SOQL, refactor Lightning components, and tune bulk-process jobs. Measured impact through pre/post performance regression SLIs (response time < 200 ms, heap usage < 2 GB).
- Triage & defect governance – participated in daily performance triage calls, automated defect classification with BERT-based NLP that tagged tickets as “SOQL”, “Governor-Limit”, “X-App Integration” etc., thereby cutting triage overload by ~25%.
- Audit-ready reporting – produced concise Test Summary Reports that met Wells Fargo’s internal governance and regulatory audit requirements.
- Continuity & reliability – integrated performance tests into a nightly CI/CD pipeline (Jenkins+GitHub) that performed “performance gates” ensuring no regression round-up of critical SLAs before any major release.
Education
Master of Science -
University of Houston - Clear Lake
USA01.2014
MSC - undefined
Osmania University
India01.2012
Skills
- Troubleshooting skills
- Azure monitor
- Test execution
- Documentation skills
Environment and tools used
- OpenAI GPT-4 / RAG NFR extraction, executive-level summaries
- XGBoost / Random-Forest Auto-priority of test scenarios & bottleneck detection
- Prophet / ARIMA Predictive anomaly detection in AppDynamics / Splunk data
- BERT / SciKit-Learn Defect classification & root-cause tagging
- Power-BI + Python Interactive KPI dashboards & automated audit reports
- JMeter’s REST Module + GPT-3 payload generator Real-world API load generation
Environment
- GPT-4 / Test.AI Test-case generation, executive summaries, scenario modeling
- Scikit-learn Data-quality scoring for migration data
- Prophet / ARIMA Time-series forecasting for latency, throughput, capacity
- XGBoost / Random-Forest Predictive regression detection & CI/CD gate logic
- BERT / GPT-4 NLP Defect ticket intent classification & triage
- New Relic / AppDynamics / Dynatrace / Splunk + ML plugins Real-time anomaly detection dashboards
- Jenkins + custom ML scripts Automatic merge gate based on performance predictions
- Power-BI + ML-derived KPIs
Timeline
Senior Salesforce Performance Engineer
Ansira
08.2020 - Current
Senior Salesforce Performance Engineer
Hertz Corporation
09.2018 - 08.2020
Salesforce Performance Tester
BBVA Bank
10.2017 - 08.2018
Performance Tester
Comcast
01.2016 - 09.2017
Performance Tester
Wells Fargo
01.2015 - 12.2015
MSC - undefined
Osmania University
Master of Science -
University of Houston - Clear Lake