Vetted System Design Professionals

“Unity/C# gameplay engineer with hands-on ownership of a 100+ quest system and measurable 20% performance gains through profiling and engine-level optimization. Also has shipped Photon-based multiplayer VR work, including a 4-user application for an NSA-related project, and has practical analytics-driven UI improvement experience.”

Bhavesh Ittadwar

Screened ReferencesStrong rec.

Senior Full-Stack Engineer specializing in scalable web and cloud systems

USA3y exp

“JavaScript engineer who built a Michelin-specific headless CMS forms platform based on apostrophe-forms, powering forms across 400+ Michelin websites. Designed an extensible, SOLID-aligned modular field architecture with a shared design system, cutting hundreds of lines of per-project code across 10+ implementations while driving cross-device compatibility and performance (BrowserStack, Lighthouse, SSR).”

Nicholas Pinero

Screened ReferencesStrong rec.

Mid-level Software Engineer specializing in Unity XR/VR training simulations

Remote, USA8y exp

“Unity/C# VR developer who owned a next-gen replay/review system end-to-end, improving determinism so recorded actions (e.g., shots) replayed consistently. Also built a Jenkins-triggered GameDriver-based VR QA automation suite that ran nightly builds and cut manual QA effort by ~75%, and contributed to Photon PUN multiuser mode with hands-on network debugging.”

“Backend/platform engineer who has owned a real-time data ingestion/processing/reporting API built with FastAPI, Redis, and Celery, including performance tuning via query/index optimization, caching, and async workers. Strong Kubernetes + CI/CD + GitOps (ArgoCD) experience, plus hands-on monitoring/logging (Prometheus/Grafana/ELK) and a Kafka/Spark real-time streaming project from their master’s program.”

“Built and shipped a production RAG system (“TROJAN KNOWLEDGE”) for answering questions over technical PDFs, using a 3-stage retrieval stack (BM25 + FAISS + cross-encoder) to lift F1 from 71% to 84%. Drove major performance gains with a 3-level cache (memory/Redis/disk) cutting latency from ~200ms to ~10ms, and added Prometheus/Grafana monitoring plus LangChain-based fallback logic to handle OpenAI rate limits under load.”

Preethi Bandari

Screened ReferencesStrong rec.

Mid-level Full-Stack Developer specializing in scalable web applications

Memphis, TN5y exp

“Developer who uses AI tools pragmatically to accelerate coding while keeping full ownership of system design and decision-making. Emphasizes rigorous review, testing, and alignment with architecture, security, and performance standards, and stays current on AI through both industry sources and hands-on experimentation.”

“Backend-leaning full-stack engineer who has built AI-powered analytics products from 0→1, including a predictive analytics dashboard and an AI orchestrator for natural-language-to-database querying. Particularly strong in making LLM systems production-safe through schema validation, self-healing retries, monitoring, and retrieval optimization, with quantified impact on cost, latency, and quality.”

“Built across cloud infrastructure, AI-powered product workflows, and backend data reliability in environments including Northeastern, Knead, and Grafx. Particularly compelling for roles needing someone who can both ship AWS-based systems end-to-end and debug messy production issues involving caching, APIs, and data pipelines.”