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Vetted Log Analysis Professionals
Pre-screened and vetted.
Mid-level Machine Learning Engineer specializing in GenAI and end-to-end ML systems
Junior Robotics & Controls Engineer specializing in SLAM, mapping, and robot navigation
Senior Cloud Security Engineer specializing in multi-cloud security and DevSecOps
Senior Information Security Analyst specializing in GRC and third-party risk
Senior Python Backend Engineer specializing in AWS serverless and data pipelines
Mid-level QA & Apple Platform Support Specialist specializing in iOS/macOS testing and security verification
Senior AI & Cloud Engineer specializing in GenAI and data platforms
Mid-level Customer Success Engineer specializing in application security and FinTech integrations
Senior Full-Stack Engineer specializing in Healthcare IT and FinTech
Mid-level .NET Developer specializing in cloud-native microservices and enterprise APIs
Mid-level Software Engineer specializing in backend services and data engineering
Mid-level Software Engineer specializing in backend systems and healthcare IT
Senior QA Tester specializing in video game QA strategy and manual feature testing
Mid-level Full-Stack Developer specializing in React and Python (Django/FastAPI)
Mid-level Full-Stack Engineer specializing in AI-powered enterprise applications
Deeksha V
Mid-Level Software Engineer specializing in cloud data platforms and FinTech payments
“Backend engineer focused on financial systems, having evolved payments and reconciliation platforms using Python/FastAPI and PostgreSQL with an emphasis on idempotency, validation, and consistency. Has led monolith-to-services migrations using feature flags and shadow traffic, and implements defense-in-depth security (OAuth2/JWT plus DB-level row security) for multi-tenant environments.”
Rafael Ortega
Senior Full-Stack & AI Engineer specializing in LLM integrations and cloud-native systems
“Backend/data engineer with hands-on production experience building FastAPI Python APIs and AWS-native platforms (Lambda/API Gateway, SQS, ECS Fargate) with Terraform + GitHub Actions CI/CD and strong reliability practices (JWT/RBAC, retries/timeouts, structured errors/logging). Also built AWS Glue ETL pipelines (S3/RDS to curated S3/Athena) with schema evolution and data quality controls, modernized legacy processing via parallel-run validation and phased cutovers, and has demonstrated SQL tuning impact (seconds to <200ms) plus incident ownership for batch pipeline SLAs.”
Mid-level Full-Stack Software Engineer specializing in enterprise SaaS
Naga Karumuri
Mid-Level Full-Stack Engineer specializing in AI and 3D computer vision
“Built and productionized an LLM-driven document verification workflow for a construction firm’s submittals process, moving from a Vercel/Next.js prototype to a FastAPI + LangChain/LangGraph backend with background workers and multi-server deployment. Uses LLM tools (e.g., OpenAI Codex/Cloud Code) for rapid development and log-driven root cause analysis, and partners with customer teams on evaluation metrics and iterative improvements.”
Vaishnavi Kashyap
Junior Full-Stack Software Engineer specializing in web platforms and sustainability analytics
“Full-stack/backend engineer who owned a production digital assembly planning platform at Saint Gobain end-to-end (React/Node/Postgres), maintaining 99.9% uptime across 5 factory sites and driving a reported 90% improvement in factory-floor coordination. Also built and operated BigQuery + Vertex AI (ARIMA) forecasting/data pipelines processing 1M+ datapoints daily, with strong emphasis on idempotency and data-quality validation to prevent incorrect outputs.”
suparshwa patil
Mid-level Software Engineer specializing in AI platforms and full-stack systems
“Built and shipped a production AI-powered Q&A/RAG onboarding assistant at One Community Global that unified knowledge across Notion, Google Docs, and Slack, cutting volunteer onboarding time by 45%. Demonstrates strong end-to-end ownership: LangChain agent orchestration integrated into a FastAPI backend, rigorous evaluation (200-query dataset, ~85% accuracy), and production feedback/monitoring with source-attributed answers to build user trust.”