Vetted Data Structures Professionals

Jack Oakley

Screened ReferencesStrong rec.

Director-level VR Game Programmer specializing in Unity and QA leadership

Austin, TX15y exp

“Unity/VR gameplay engineer who has shipped full games and built a robust coin economy + massive gear customization system (over a million combinations) backed by a cloud service. Notably discovered and fixed a severe backend security flaw by adding proper authentication (security key + OAuth2), and has hands-on VR performance optimization experience (OVR Metrics, LOD/mipmaps, imposters) plus multiplayer sports sync design using Photon PUN2.”

Kruti Mehta

Screened ReferencesStrong rec.

Mid-level Software Engineer specializing in backend systems and FinTech analytics

Maryland, USA5y exp

“Engineer with a pragmatic, high-leverage approach to AI-assisted development: uses AI and multi-agent workflows aggressively for implementation and internal tooling, while maintaining strict human oversight for user-facing features. Stands out for treating agents like junior engineers, breaking work into actionable tasks, and combining robust testing, local E2E validation, and feature-flag rollouts to safely ship production code.”

Syed Mujtaba

Screened ReferencesStrong rec.

Senior Frontend Developer and MERN Stack specialist

Pakistan9y exp

“Frontend engineer experienced leading client-facing portals (including WebGL integrations) and admin panels, with a strong focus on scalable architecture (monorepo, shared design system/atomic design) and performance. Has built complex React+TypeScript workflows like multi-step user creation with dynamic forms, and ships major features (e.g., video recording) using feature-flagged, trunk-based delivery and E2E testing.”

“Unity gameplay engineer with hands-on ownership across core game systems, multiplayer, AI/LLM integration, and cross-platform XR/mobile shipping. Particularly compelling is their combination of deep runtime optimization and systems architecture: they cut match-3 cascade frame spikes by 40-50%, eliminated a class of production race-condition bugs, and also built a local-model FastAPI-backed AI prototype with structured evaluation and cost control.”

Mudit Devenkumar Choksi

Screened ReferencesModerate rec.

Junior Full-Stack Software Engineer specializing in web apps, automation, and cloud systems

Louisville, KY2y exp

“Engineer with hands-on experience owning end-to-end industrial automation deployments and real-time data systems. Most notably led a multi-million dollar warehouse automation implementation that reduced manual intervention by 25%, while also building streaming text analytics pipelines and strengthening production reliability through robust observability and pipeline controls.”

“Backend engineer focused on real-time, event-driven distributed systems (Node.js/TypeScript) with strict latency and reliability requirements. Deep hands-on experience debugging concurrency issues and designing resilient workflows (idempotency, circuit breakers, compensating actions) with strong observability; familiar with ROS/ROS2 concepts and confident ramping into robotics integrations.”

Vengalarao Pachava

Screened

Junior AI Data Engineer specializing in Azure Databricks lakehouse and GenAI RAG systems

Irving, TX2y exp

“Backend/applied AI engineer from Cloud Rack Systems who built production GenAI/RAG and data platforms on Azure/Databricks at enterprise scale (2.5M records/day). Known for making LLM systems behave like deterministic services via strict retrieval contracts, citation-based validation, and strong observability—shipping a knowledge assistant used daily by 50+ users while driving hallucinations near zero and materially improving latency and cost.”

“Backend engineer who built a production retrieval-augmented narrative analysis platform for 100-page screenplays using a Node/Express orchestrator and a Python/FastAPI AI engine, including a key redesign from disk-based uploads to in-memory streaming to eliminate Windows file-lock failures. Also led a refactor of a municipal vehicle tracking system into a C-based distributed engine handling 4M+ daily packets with 99.99% data integrity and automation that reduced manual ops by 50%.”

“Backend/AI engineer who built Matchable, an end-to-end AI-powered workforce matching platform using FastAPI, transformer-based NLP, PostgreSQL, and AWS, with a strong focus on practical system design tradeoffs. Also brings research-oriented experience from Los Alamos/ASU simulation work and has built multi-agent LLM workflows with schema validation and auditability, suggesting a thoughtful approach to reliability in AI systems.”