Vetted Deep Learning Professionals

“Built and productionized a MediCloud/Medicoud LLM microservice platform that lets clinicians query medical data in natural language, orchestrating multi-step RAG-style workflows with LangChain and evaluating/debugging with LangSmith. Delivered measurable gains (consistency ~70%→90% / +20%; latency ~2.0s→1.1s / -40%) by implementing structured prompts, fallback logic across multiple LLMs, hybrid retrieval tuning, and AWS Lambda performance optimizations (package size, async, caching).”

“Full-stack product engineer who has shipped AI-powered job board moderation and validation features end to end across React/TypeScript, serverless backends, and Postgres. Stands out for combining UX polish, LLM-backed workflow design, and reusable async infrastructure patterns to improve reliability, speed of delivery, and user participation.”

Harshit Vashisth

Screened

Junior AI Engineer specializing in LLMs, RAG systems, and MLOps

Remote, United States1y exp

“Robotics software engineer who built an end-to-end system ("justmatrix"), focusing on multi-agent orchestration and a multi-RAG retrieval backend/API. Has hands-on ROS experience, including a custom node for reliable high-frequency sensor data routing, plus deployment automation using Docker, Kubernetes, and CI/CD.”

Jaiden Kettleson

Screened ReferencesStrong rec.

Entry-Level Full-Stack & AI Engineer specializing in chatbots and web apps

1y exp

“Data Science honors graduate (Maryville University) who has built Python/SQL backends and a capstone website handling sensitive user data. Emphasizes secure data handling (password encryption, secure database updates) and uses Git/GitHub Pages with CI/CD-style practices for managing and deploying changes.”

“Built and deployed a production bilingual (Bengali/English) AI virtual assistant that replaced IVR for telecom customer service at massive scale (~15M users), integrating ASR/TTS, Rasa dialogue management, and custom NLP. Overcame low-resource Bengali data and noisy call-center audio with synthetic data augmentation and transformer fine-tuning, achieving significant production gains including ~50% reduction in support calls.”

Muhammad Murtaza Murtaza

Screened

Mid-level Machine Learning Engineer specializing in NLP, Computer Vision & Predictive Analytics

Islamabad, Pakistan5y exp

“Built a production LLM fine-tuning pipeline for domain-specific code generation at Pigeonbyte Technologies, including automated collection and rigorous quality filtering of 10M+ code samples (AST validation, sandbox execution/testing, deduplication, drift monitoring, and human-in-the-loop review). Also implemented end-to-end ML orchestration in Apache Airflow with data quality gates, dataset versioning in S3, benchmarking, and automated model promotion, and has a reliability-first approach to agent/workflow design.”

Maham Saleem

Screened

Senior Game Developer specializing in Unity, multiplayer, and AR/VR

Bahawalpur, Pakistan8y exp

“Unity/C# gameplay engineer with strong ownership of real-time multiplayer systems, highlighted by building the server-authoritative Photon Fusion architecture for Kawamon.io end-to-end. Brings a useful mix of multiplayer networking, mobile/VR optimization, and AR pipeline experience, plus technical leadership through code reviews, standards, and mentoring.”

“FAU-based analytics candidate with hands-on academic project experience across SQL data preparation, Python/NLP sentiment analysis, and predictive modeling. They stand out for turning messy datasets into clean reporting tables, building reproducible analysis workflows, and translating findings into practical recommendations around operations, credit risk, and marketing ROI.”

Jithin Mathew

Screened

Mid-level Robotics & Embedded Systems Research Engineer specializing in edge AI for precision agriculture

Fargo, ND5y exp

“Robotics lead engineer (PhD/agriculture research context) who built and iterated a three-unit, wirelessly coordinated greenhouse data-collection robot—solving real-world alignment and comms reliability issues by redesigning the rail/gear system and moving from RF to ESP Wi-Fi with MAC-addressed control. Strong embedded C/C++ background with full-stack robot control implemented from scratch; beginning exposure to ROS 2 via an upcoming field deployment.”

“AI/ML practitioner with production experience building a RAG-powered contextual customer support agent, optimizing for low latency using vector databases and smaller LLMs. Also deployed a fraud detection model on Kubernetes with auto-scaling for heavy transactional loads, and improved chatbot accuracy by 15% through metric-driven testing and evaluation. Partners with Marketing on personalization/recommendation initiatives with measurable outcomes tied to customer feedback.”

Mohammed Elwaei

Screened

Junior Software Engineer specializing in systems, compilers, and AI research

Tampa, FL2y exp

“Engineering student with hands-on robotics and simulation experience: led an Arduino line-following “Batmobile” robot project used as a K–12 teaching tool and won best design in a 100+ student section. Also implemented SARSA reinforcement learning for a 16-DOF robotic hand in MuJoCo, optimizing the state representation to train efficiently on a CPU, and brings strong cloud/container skills (Docker, Kubernetes, AWS certs).”

“AI engineer with hands-on experience shipping production systems across semantic search, RAG/LLM applications, and computer vision. Built a personalized e-commerce search platform with measurable relevance and latency gains, and deployed grounded GenAI chat systems that significantly reduced hallucinations while lowering support burden. Also brings edge-deployment experience in monocular depth estimation and 3D reconstruction, suggesting strong breadth across modern applied AI.”

“Built a Smart Resume Screening tool with a React frontend and a Python backend, owning most backend architecture and delivery. Implemented FastAPI endpoints for file upload and NLP/ML inference, created the end-to-end resume classification pipeline, logged predictions to a database for accuracy tracking, and deployed a Dockerized service optimized for low-latency, concurrent processing.”