Vetted Root Cause Analysis Professionals

“Engineering Manager/Tech Lead on Grindr’s monetization team who helped ship an AI-powered conversation summary feature (A-list), contributing across Android freemium implementation and backend LLM workflow service architecture/reviews. Demonstrated strong operational ownership by leading a Boost production incident from detection through rollback and prevention, and improved team throughput by introducing a lightweight end-to-end delivery process in a high-growth environment.”

Surya Teja

Screened

Mid-level Backend/Full-Stack Engineer specializing in AI and FinTech payments

Tempe, AZ4y exp

“Full-stack engineer who has owned an operational reporting/dashboard product end-to-end—building a React UI, designing/implementing FastAPI services, and deploying/operating on AWS. Demonstrates strong performance engineering (Postgres query/index tuning using EXPLAIN ANALYZE) with concrete impact (reports reduced from tens of seconds to a few seconds) and a reliability mindset across observability, migrations, and resilient third-party/ETL integrations.”

“Full-stack AI engineer who has built both document-intelligence products and agentic investigation systems end to end. At ControlRooms.AI, they helped ship a production-facing root cause investigation workflow for industrial operations using Neo4j, FastMCP, RAG, OCR/VLM inputs, and multiple LLMs, contributing to roughly a 10x reduction in manual investigation time. They stand out for designing explainable, traceable AI systems that surface evidence, uncertainty, and missing context rather than forcing overconfident answers.”

“Safety-critical firmware validation engineer with Tesla autonomous vehicle experience who built Python-based HIL/SIL automation and dashboards, cutting regression time by 30% while maintaining an auditable risk-tradeoff process with safety and engineering teams. Also deployed an inventory management system across 8+ R&D teams in 3 countries at FUJIFILM, troubleshooting a major cross-site sync issue to a timezone root cause with strong documentation and interim mitigations.”

“IBM Power/AIX infrastructure engineer who has owned a large enterprise footprint (40 Power8/9 frames, 400+ AIX LPARs) with deep hands-on VIOS/HMC, NIM, performance tuning, and PowerHA recovery. Demonstrated high-impact incident response (avoided DB reboot saving ~4 hours; restored clustered services in <20 minutes) plus strong RCA and preventative remediation. Also brings modern DevOps/IaC experience building GitHub Actions pipelines and Terraform-managed AWS EKS/VPC/RDS/S3 environments.”

“Built a production AI-powered workflow automation system at Netflix that integrated OpenAI and LangChain with FastAPI services on AWS, cutting roughly 320 hours of manual operational effort. Brings a mix of full-stack product development and practical AI systems experience, with strong attention to reliability, maintainability, and non-technical user adoption.”

“Backend owner at Apple for an AI workflow orchestration service, with hands-on experience stabilizing peak-traffic production systems using OpenTelemetry-style tracing, bounded async concurrency, and database performance tuning. Built and shipped a Python LLM-agent orchestration layer to automate multi-step operational workflows, emphasizing guardrails, auditability, and deterministic fallbacks to keep non-deterministic AI behavior production-safe.”

Derek Tuggle

Screened

Executive Robotics & Machine Learning Engineer specializing in industrial IoT controls

San Francisco, CA6y exp

“VP of New Product Development at Axiom Cloud who built and scaled a "Virtual Battery" product that used supermarket frozen inventory as thermal energy storage—personally prototyped core control/safety logic in Python and led the engineering buildout through deployment and operations. Combines real-world industrial controls and edge deployment experience (LonWorks/Modbus, Docker/CI/CD) with an MS in CS focused on robotics, perception, and ML, including ROS 2 and YOLO-based perception.”

Jehanzeb Khan

Screened

Director-level Engineering Manager specializing in large-scale data and compute platforms

Sunnyvale, CA20y exp

“Platform and distributed-systems leader (player-coach) who owned architecture and reliability for an Amazon analytics/data platform serving ~100K internal users at exabyte scale. Built an ML-driven “Lakeflow” optimization layer that cut pipeline completion times ~20–25% and reduced compute waste >15%, and led major incident response/redesign efforts (e.g., deletion storm) with strong rollout/observability/rollback practices.”

“Built GenMedX, a multi-module clinical AI system for emergency department decision support spanning triage prediction, diagnosis, medication Q&A, and visit summarization. Stands out for combining medical LLM fine-tuning, RAG, and rigorous evaluation/monitoring to drive a major triage recall improvement from 38.5% to 76.6%, with a strong focus on safety, edge-case detection, and production reliability.”

“Operations leader with Amazon experience owning a founder-level initiative during national supply chain Regionalization, building and scaling a "Stack-to-Light" operating mechanism to standardize non-sortable FC execution with instrumentation and balanced-scorecard metrics. Later joined Revivn to stabilize an underperforming operation by developing frontline leaders and aligning ops with finance and GTM through clear KPIs and operating cadence.”

“Product/UX designer with a CS/software engineering background at Microsoft (former intern, now full-time) who bridges design and implementation. Built Shiproom (a Twitch-like live-coding platform) end-to-end with a custom Atomic Design-based system, and has shipped impactful CRM improvements in Dynamics 365 for 60k+ support agents using TypeScript and C#. Also worked on a Microsoft Global Hackathon project integrating a Copilot-backed sign language translation model to reduce documentation friction for deaf support engineers.”

“AWS engineer who productionized an internal ML-driven data pipeline from a notebook prototype into a scalable, observable Python service (schema validation, deduplication, idempotency, safe retries, versioned transforms, CloudWatch alarms), reducing manual effort and improving data accuracy/trust. Experienced diagnosing workflow issues in real time (e.g., upstream schema changes) and partnering with account managers/support to unblock adoption of seller-facing Marketplace features by demonstrating reliability with concrete metrics.”

“Architect/engineering leader who built a multi-tenant AI platform end-to-end, including a secure FastAPI orchestrator (JWT, RBAC, tenant isolation, auditing) and an extensible MCP tool-routing layer, then productionized it via fully containerized microservices (Docker, Postgres/pgvector, Redis). Also has strong governance and compliance experience (ARB with security/privacy/SOX) and has owned high-severity incidents through mitigation and RCA/RCCA, plus prior high-volume payments/accounting data pipeline design with audit-grade integrity checks.”

“AppSec/customer security specialist with Atlassian enterprise cloud migration experience, advising on SSO and API token hardening and driving adoption through phased rollouts. Implemented Snyk (SCA) and SonarQube (SAST) in Bitbucket Pipelines with Jira-based vuln workflows, cutting critical vuln MTTR from 30 to 7 days for financial services customers. Strong in SSO troubleshooting (Okta/SAML) and secure AWS/EKS-based agent integrations with secrets management and Datadog observability.”

Tejaswini Manjunatha

Screened

Mid-level Reliability Engineer specializing in incident response and LLM-driven support automation

Palo Alto, USA4y exp

“Customer Success Services / Support professional working on Palantir Foundry who productionizes customer integrations (secure OAuth2, scheduled pipelines) and builds LLM-driven support automation (runbook matching) with monitoring and evaluation suites. Also leads developer workshops/demos on Foundry packaging/installation workflows, using live debugging techniques to make concepts concrete.”

“Candidate describes a development approach centered on AI-assisted coding, testing, and agent-driven workflows, including production exposure to multi-agent systems and governance-oriented logging. They appear particularly focused on combining AI speed with structured validation through unit tests, boundary tests, and edge-case monitoring.”

Yashwanth J

Screened

Mid-level Software Engineer specializing in AI/ML and full-stack systems

Seattle, WA4y exp

“Engineer with Apple experience building LLM-powered internal workflow orchestration systems using Python, LangGraph, FastAPI, Redis, vector search, and Kubernetes. Stands out for a highly pragmatic, production-focused approach to agentic systems: deterministic state management, strong guardrails, observability, and human review for high-risk actions.”

Casey Conroy

Screened

VP of Regulatory Reporting specializing in FR Y-14 and SEC filings

New York, NY8y exp

“Finance/real-estate professional currently at Citi supporting FRB exam work (transaction testing, data-quality root cause analysis) and coordinating cross-functional regulatory responses with executive communications. Previously at JPMorgan Global Real Estate, automated an Excel-based depreciation forecast for a commercial office portfolio using complex formulas to handle overlapping project-phase data.”

“Bootstrapped-startup CTO and former founding engineer who has built products end-to-end as a solo developer across web and games. Led Grublify’s Next.js/TypeScript site (Strapi + Shopify), including a blog that reached ~5k monthly visitors and performance work like progressive loading/infinite scroll. Also implemented a resilient USDA nutrition ingestion pipeline with retries, partial-failure handling, and computed nutrient metrics.”

Chaithanya Konda

Screened

Mid-level Data Engineer specializing in multi-cloud analytics platforms

Waltham, MA6y exp

“Data engineer with hands-on GCP platform experience spanning BigQuery, Cloud SQL, Dataflow, and Cloud Composer, including both production operations and cloud migration work. They led a migration from legacy SQL Server/Oracle systems to a cloud-native BigQuery architecture and cite measurable impact: processing reduced from hours to minutes, query latency improved 60%+, and ingestion time improved 40%.”

Ethan Hu

Screened

Entry-level Software Engineer specializing in full-stack and embedded systems

Seattle, WA1y exp

“Backend/full-stack engineer on Qualtrics' Online Samples team working on audience sampling systems and APIs used by researchers. They have hands-on ownership of TypeScript/React/Express services, emphasize multi-layer testing and production observability with Splunk/VictorOps, and have built APIs for both internal and external developers.”