Vetted Neural Networks Professionals

“Built DocMind, an end-to-end PDF chat assistant using React/TypeScript, FastAPI, and Postgres/pgvector, showing full-stack ownership plus practical performance tuning and AWS debugging skills. At Social Tech Labs, improved onboarding, shipped lean under ambiguity, and created a reusable low-latency feature serving layer that reduced duplicated infrastructure work across models.”

“LLM/agent systems builder who deployed a production hybrid router for immerso.ai that dynamically selects retrieval vs reasoning vs generative pathways, achieving an 82% factual-accuracy lift. Deep hands-on experience optimizing local Mistral 7B inference (4–5 bit GGUF quantization, KV-cache reuse) and building reliable RAG/agent workflows with LangChain/LangGraph/AutoGen across GCP Cloud Run and AWS (ECS/Lambda).”

Pranav Mishra

Screened

Junior Machine Learning Engineer specializing in LLM agents, RAG, and MLOps

Charlotte, NC2y exp

“AI/ML engineer who has shipped production systems across computer vision and conversational agents: built a YOLOv8-based wheel fitment pipeline at a Techstars-backed automotive startup, focusing on sub-second latency, monitoring, and robust fallback mechanisms that drove 2–3x page view growth and +5–6k users. Also built a voice-based interview platform orchestrating Deepgram + GPT-4 Mini + OpenAI TTS with FSM-driven reliability, and has hands-on RAG experience (LangChain, hybrid retrieval, cross-encoder reranking, custom pseudo-query generation).”

Nischit Patel

Screened

Mid-level Software Development Engineer specializing in Python, APIs, and AWS

NC, US5y exp

“Backend engineer with experience modernizing legacy systems and building modular Python/Flask services, including a REST-to-GraphQL migration for an e-commerce platform that improved API response time by 45%. Strong in performance and scalability work across PostgreSQL/SQLAlchemy (indexing, JSONB, N+1 fixes, connection pooling) and high-throughput systems (Celery + Redis), plus integrating ML microservices with TorchServe, Kafka streaming, feature stores, and Prometheus/Grafana monitoring.”

“Built and owned a production RAG-based internal knowledge assistant end-to-end, from experimentation through cloud deployment and monitoring. Demonstrated strong practical GenAI judgment by choosing prompt optimization and retrieval tuning over fine-tuning for dynamic data, driving a 40% to 50% reduction in time to answer while improving relevance, lowering hallucinations, and increasing productivity.”

Ahmad Alomari

Screened

Senior Data Scientist & Machine Learning Engineer specializing in computer vision and production ML

Cleveland, OH7y exp

“PhD in computer engineering who has built production-oriented ML/NLP systems for space-weather prediction using Spark-based ETL on noisy satellite sensor logs. Strong in entity resolution and semantic search—fine-tuned E5 embeddings with contrastive learning and deployed to Pinecone, improving top-5 retrieval precision by 25%—and emphasizes scalable, observable pipelines with Airflow, Docker, and CI/CD.”

HarshaVardhanRao Vemuganti

Screened

Junior AI/ML Engineer specializing in Generative and Agentic AI

Lafayette, Louisiana4y exp

“Built and deployed a production-grade LLM agent for credit management and accounts receivable automation, integrating ERP/MySQL data via a RAG pipeline and exposing services through FastAPI with Pydantic-validated outputs on AWS Bedrock. Emphasizes reliability and compliance for financial operations using schema validation and human-in-the-loop review, reporting ~32% reduction in manual work and ~41% improvement in response time/reliability.”

Hamdy Elmorsy

Screened

Junior Autonomous Driving Perception Engineer specializing in sensor fusion and SLAM

4y exp

“Robotics software engineer with thesis work optimizing the Autoware perception pipeline via DDS/synchronization tuning for lower latency and better throughput, plus hands-on ROS1/ROS2 experience deploying perception (YOLOv8) and SLAM/localization on real vehicles and delivery robots. Has practical debugging depth (Kalman filter crashes, transformation/scan-matching issues) and CI/CD automation experience with Jenkins.”

“Analytics-focused candidate from SWAP PM with hands-on experience turning messy onboarding and user behavior data into reliable funnel and retention reporting using SQL and Python. They owned an end-to-end onboarding improvement project, aligned stakeholders on metric definitions, and helped drive changes that reduced onboarding time by about 45%.”

“AI/ML engineer with 9+ years of experience building production recommendation and LLM systems end-to-end, from experimentation through deployment, monitoring, and retraining. Stands out for combining strong MLOps discipline with practical GenAI/RAG implementation, including measurable impact such as ~25% higher engagement on an e-commerce recommender and nearly 30% faster knowledge retrieval from internal documents.”

“ML/AI engineer with hands-on experience building healthcare and fraud-detection systems from experimentation through deployment, monitoring, and retraining. Stands out for combining real-time IoT pipelines, cloud-native MLOps, and GenAI/RAG in regulated healthcare settings, with reported impact including reduced emergency response times and a 25% reduction in manual diagnosis time.”