Vetted GraphQL Professionals

“Full-stack engineer (T-Mobile experience) focused on authentication/session-management systems, with hands-on work optimizing token-validation flows and reducing latency by eliminating redundant API calls and adding caching. Brings strong production ownership with observability (Splunk/Grafana), Postgres data modeling/index tuning, and resilient async workflow design (idempotency, retries/backoff, queues).”

Khaliun Gerel

Screened ReferencesStrong rec.

Senior Full-Stack Engineer specializing in cloud, web, and mobile platforms

New York, NY7y exp

“Full-stack product engineer who has owned end-to-end delivery of multi-client platforms: Finy (agriculture platform with 3 role-based web dashboards plus 2 field mobile apps) and Ugoku (Japanese studio platform with React/TypeScript dashboards, Node/Mongo backend, and mobile AR video playback). Strong in scalable architecture and performance—offline-first mobile for low connectivity, and AWS-based asynchronous video/AR processing with S3/CloudFront—plus building internal ops tools adopted quickly due to measurable workflow improvements.”

Osaze Edo-Osagie

Screened ReferencesStrong rec.

Senior Frontend/Full-Stack Engineer specializing in React and TypeScript

New York, USA7y exp

“Frontend engineer from Meltwater who led delivery of a universal post details modal used across multiple apps, integrating three different frontend frameworks (Vue, React, StencilJS) into a plug-and-play StencilJS feature. Built scalable, reusable UI components and even helped create an internal CSS library/design system, with a strong focus on performance optimization and reliable QA-driven rollouts.”

Zia Karim

Screened ReferencesStrong rec.

Executive AI and technology leader specializing in regulated enterprise and FinTech

Stamford, CT26y exp

“Founder with 10+ years in the startup ecosystem who is actively building an early-stage company and raising capital through partners in Singapore, Dubai, and LatAm. Deeply familiar with VC and accelerator models but intentionally pursuing a partner-led, B2B enterprise route to accelerate revenue, preserve control, and leverage direct customer and distribution access.”

“Full-stack engineer with hands-on experience spanning real-time AI products, large-scale payments migration, internal research infrastructure, and open-source ML tooling. Particularly compelling is the mix of low-latency React/Node/TypeScript systems work, zero-downtime migration of 50,000 accounts across 12 regions, and proactive contributions to Kubeflow build and security reliability.”

Sreenaina Koujala

Screened ReferencesStrong rec.

Mid-level Full-Stack & AI Engineer specializing in cloud and intelligent systems

Ashburn, VA8y exp

“Builder with experience across government contracting, engineering automation, and solo AI product development. They architected a serverless AWS pipeline that converted unstructured BIM data into IFC 3D models, built an enterprise internal-data chatbot with auditability and guardrails at Steampunk, and independently launched an AI study platform using Claude. Strong fit for early-stage or ambiguous environments where end-to-end ownership and practical AI systems matter.”

“Zero Trust security product lead focused on UI/API delivery, stability, and customer adoption at enterprise scale, including deployments serving 1200 customers. Stands out for hands-on production debugging across the full stack, customer-facing incident ownership, and a pragmatic approach to turning failures into automated regression coverage.”

“Lead front-end engineer focused on large-scale React microfrontend enterprise platforms, with experience spanning telecom e-commerce and financial services. Stands out for combining architecture ownership with deep browser-level performance expertise, including a 42-45% route transition improvement and UX changes that cut workflow completion times by about 25% for demanding institutional users.”

“At Esri, the candidate is building a production LLM-powered WebGIS AI framework that embeds an AI assistant into web maps and routes natural-language requests into ArcGIS JavaScript SDK functions via a LangGraph-orchestrated, multi-agent system. They emphasize production reliability and scale (strict tool calling/JSON, live schema validation, query guardrails) and rigorous evaluation/observability using LangSmith, offline prompt datasets, and latency/tool-call accuracy tracking.”

Alp Komban

Screened

Junior Machine Learning Engineer specializing in computer vision for medical imaging

Mountain View, CA2y exp

“Applied ML/LLM practitioner working in healthcare-facing products, using RAG and LoRA fine-tuning on medical data and implementing production monitoring (confidence scoring) for clinician oversight. Has hands-on experience debugging agentic/LLM pipelines (including OCR preprocessing fixes) and regularly delivers technical demos to doctors, investors, and conferences—contributing to adoption and even helping close a funding round through end-to-end pipeline walkthroughs.”

“Built a production-grade agentic document intake system that converts PDFs into structured records with strict schema validation, confidence-based retries, and a human review UI. Demonstrates strong practical judgment around making LLM systems reliable in enterprise workflows, including custom orchestration, observability, and continuous evals rather than relying on off-the-shelf abstractions.”

“Backend/platform engineer who owned high-volume Java/Spring Boot microservices on AWS (Kafka + RDS/DynamoDB) and has hands-on experience debugging complex production latency incidents across DB, JVM/GC, and async consumers. Also shipped applied AI features for ops, including an LLM-powered log analysis assistant and an incident-response agent with strong safety guardrails (schema-validated tool use, retries/backoff, and human-in-the-loop escalation).”

“Backend/AI engineer at Intuit who built a production AI-powered case assistant for support agents (FastAPI on AWS EKS) combining Postgres case data, OpenSearch retrieval with embedding reranking, and internal LLMs. Improved peak-season reliability by diagnosing P95/P99 timeout spikes and cutting P95 latency from ~800ms to <400ms via composite indexing, keyset pagination, connection pool tuning, and caching, while adding grounded-generation guardrails (evidence packs, confidence thresholds, fallbacks, human-in-the-loop).”

“FinTech/AI engineer who has shipped an end-to-end discrepancy-detection product for financial managers using Next.js, FastAPI/GraphQL, Pinecone, and AWS (with dev/staging/prod, observability, A/B testing, and documentation). Also built an AI-native “AI Genesis” system with agentic cyclic workflows, routing, and tool use, and has experience modernizing legacy systems via the strangler fig pattern while coordinating with senior stakeholders on a 5G autonomous simulation platform.”