Vetted PyTorch Professionals

Tam Do

Screened

Junior Robotics Software Engineer specializing in ROS2 perception and manipulation

Tampa, FL3y exp

“Robotics software engineer who built an assistive Kinova Gen3 manipulation system for activities of daily living, spanning RealSense+ArUco perception, MoveIt2-based motion planning/task sequencing, and a React Native tablet UI with rosbridge and voice control. Optimized real-robot trajectories by blending OMPL with Cartesian/PILZ planning and created simulation workflows to test without lab/robot availability.”

“Built and shipped production LLM-driven pipelines for clinical data processing, turning semi-structured inputs into validated structured outputs for downstream analytics. Emphasizes predictability and safety via strict JSON schemas, state-machine orchestration, backend-controlled tool calling, and robust fallbacks (rule-based checks/manual review) plus monitoring and offline/online evaluation loops; also has experience hardening workflows against messy ERP/finance data with idempotency and state tracking.”

“Unity/C# gameplay and rendering engineer who built a custom per-object shadow system in URP that improved performance from roughly 30 FPS to 80+ FPS while preserving high-quality dynamic shadows. Also built a personal multi-process AI/LLM workflow platform with custom prompt/control protocols, streaming UX, and cost-optimized memory/caching architecture—showing unusual depth across both real-time graphics and applied AI tooling.”

Sai Vamsee Bandaru

Screened

Entry-level Data Analyst and AI Engineer specializing in machine learning and LLM systems

New York, NY1y exp

“Founding-engineer-oriented full-stack product engineer who built an AI tutor system end-to-end, spanning React UI, FastAPI backend, retrieval/LLM pipelines, and Postgres optimization. Stands out for combining product thinking with deep systems work: improving onboarding and activation, shipping quickly with beta users, and abstracting reusable retrieval infrastructure for multiple use cases.”

Citlalli Trejo

Screened

Entry-level Full-Stack Developer specializing in web, iOS, and ML applications

Buena Park, CA1y exp

“Builder-minded software engineer who has shipped a live iOS budgeting app and a solo full-stack web app, with strong evidence of production discipline in both ML and fintech-style integrations. Particularly compelling for roles involving ambiguous product problems, third-party API reliability, and end-to-end ownership from backend logic through user-facing recovery flows.”

“Backend/ML engineering candidate focused on fintech automation who architected a zero-to-one agentic/LLM-enabled system to reconcile messy financial documents and bank transactions, reporting ~40% operational efficiency gains. Experienced migrating monoliths to event-driven microservices with incremental rollout via reverse proxy, and implementing production-grade security (OAuth2/JWT, RBAC, Supabase RLS) plus resilience patterns (timeouts/retries under concurrency).”

“Backend engineer who built and scaled a FastAPI-based backend for an AI-driven maintenance system automating vendor sourcing/bidding/communication. Emphasizes async, message-driven architecture with strong observability and state-machine-driven workflows, plus robust webhook/idempotency patterns to prevent duplicate/out-of-order events from causing bad bids or state changes.”

eman akram

Screened

Junior Full-Stack Developer specializing in AI/ML and mobile/web apps

Wah Cantt, Pakistan2y exp

“Built an end-to-end car maintenance application from design through deployment using TypeScript/React with a Node.js + Firebase backend, including OBD integration. Uses mock servers and a layered frontend architecture (UI/state/services separation) to iterate quickly on UX without being blocked by backend or hardware dependencies.”

Ekta Mulkalwar

Screened

Junior Machine Learning & Full-Stack Engineer specializing in applied AI systems

Los Angeles, CA2y exp

“Master’s thesis focused on building and deploying a gait-based biometric authentication system using mobile accelerometer time-series data as an alternative to passwords/2FA. Emphasized real-world robustness by addressing sensor noise and variability (phone placement, walking speed, footwear) and improving safety using biometric metrics like FAR/FRR and EER, while collaborating closely with a non-ML thesis advisor.”

“Internship-built full-stack systems spanning HR employee-record portals and internal data-quality dashboards (Flask + SQL + React), emphasizing data integrity and rapid MVP iteration. Also implemented Flask microservices with RabbitMQ for distributed task processing, addressing duplication/ordering issues with idempotency, durable queues, and correlation-ID logging; delivered quantified productivity gains for HR teams.”

Gowtham Manikanta Chilukabathula

Screened

Entry-level AI and Data Analyst specializing in LLMs and analytics

Memphis, TN1y exp

“Candidate brings a blend of AI, analytics, and go-to-market support experience through an AI/data internship and graduate assistant role. They analyzed data across 50+ organizations to identify high-fit outreach segments, improving targeting efficiency by about 28%, and also built/reviewed GPT-4 and LangChain-based outbound messaging systems with strong quality controls.”

“Early-stage product engineer who built an AI persona chat system end to end at Super Intro, spanning Next.js frontend, GraphQL/real-time backend, retrieval memory, and LLM-based matching. They combine strong TypeScript rigor with practical AI systems design, and cite measurable impact including ~40% engagement growth, ~30% recall improvement, and lower LLM costs in production.”

“Python backend developer from Larix Technologies who built and scaled microservice APIs for an omnichannel messaging SaaS (WhatsApp/Instagram/Facebook) and led production performance fixes during peak traffic, cutting webhook latency ~50%. Also shipped applied AI products end-to-end: a RAG-based PDF assistant (LangChain + Mixtral via Groq + React) and a BI agent that plans/executes/verifies multi-step analytics with strong guardrails and auditability.”