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Vetted GraphQL Professionals
Pre-screened and vetted.
Senior Full-Stack Engineer specializing in cloud-native SaaS platforms
Senior Backend Engineer specializing in Python and AWS cloud-native systems
Mid-level Backend Software Engineer specializing in scalable systems and healthcare workflows
Junior Software Engineer specializing in full-stack, cloud, and AI systems
Senior Full-Stack Engineer specializing in cloud-native enterprise applications and ServiceNow ITSM
Senior Software Engineer specializing in cloud platforms and real-time collaboration
Senior Software Engineer specializing in full-stack web platforms, SEO, and microservices
Senior Software Engineer specializing in distributed systems and agentic AI platforms
Mid-level UI Developer specializing in real-time dashboards for FinTech and Healthcare
Senior Data Engineer specializing in cloud-scale pipelines and legal data utilities
Staff Full-Stack Software Engineer specializing in cloud-native microservices
Director of Architecture specializing in AdTech, AI/ML, and large-scale cloud platforms
Executive IT & Software Development Leader specializing in cloud-native transformation and trading platforms
Junior Software Engineer specializing in SaaS analytics and reporting
Mid-level Full-Stack Developer specializing in cloud-native FinTech and Healthcare IT
Devisri Veeramachaneni
Senior Software Engineer specializing in cloud backend systems and LLM-powered agents
“Amazon Fire TV Devices engineer who built and shipped a production LLM-powered lab triage and validation system that grounds recommendations in internal runbooks/known-issue data and pushes evidence-based actions via dashboards and Slack. Emphasizes safety and measurability with structured JSON outputs, replay-based evaluation on historical incidents, and production metrics (e.g., disagreement rate and time-to-first-action), plus cost/latency optimizations like caching, batching, and rule-based fast paths.”
Nagatheja Sharaf
Mid-Level Software Engineer specializing in cloud-native systems, automation, and LLM-enabled robotics
“React-focused engineer who built a full-stack analytics/test-metrics dashboard (React frontend + Python backend) and turned common UI pieces (data tables, filter panels, chart wrappers) into a reusable internal component library with docs, examples, and basic tests. Strong on profiling-driven performance optimization (React Profiler, memoization) and on owning ambiguous internal-tool projects end-to-end; now planning to package internal patterns into public open-source components.”
Vyas Cholayil
Mid-Level Software Engineer specializing in Python automation, DevOps, and microservices
“Backend-focused engineer who built an internal wiki LLM chatbot end-to-end using FastAPI, Kubernetes, and ChromaDB vector search, including frontend integration. Also has strong DevOps/migration experience—automating large work-item and repo migrations (Jira/FogBugz/ADO on-prem to cloud) via Python scripts, JSON mappings, REST APIs, and validation test suites.”
Leela Tikkisetty
Mid-level Software Engineer specializing in ML platforms and cloud-native backend systems
“Software engineer with experience at Google and the City and County of San Francisco building production AI systems, including a RAG-based internal support chatbot and ML-driven ticket priority tagging. Has scaled data/ML platforms with Airflow on GCP (1M+ records/day, 99.9% SLA) and deployed multi-component systems with Docker and Kubernetes (GKE), using modern LLM tooling (LangChain/CrewAI, Claude/OpenAI, Pinecone/ChromaDB, Bedrock/Ollama).”
Chetana Reddy Yellareddy
Mid-Level Software Engineer specializing in distributed systems and cloud-native platforms
“Backend/AI engineer who built and scaled an internal AMD semiconductor manufacturing microservice platform (SMR), reworking a synchronous lot-request workflow into an event-driven RabbitMQ/Celery/FastAPI pipeline. Diagnosed and fixed peak-load reliability issues using deep observability and Kubernetes autoscaling, cutting notification latency back to sub-second and reducing duplicates via idempotency/DLQs. Also shipped an LLM-powered natural-language search with schema-constrained JSON outputs and guardrails, plus a plan-execute-verify Jira bug-resolution agent that can propose fixes and raise PRs under restricted permissions.”