Vetted Neural Networks Professionals

Parth Vadera

Screened

Senior AI/ML Data Scientist specializing in recommender systems, LLMs, and MLOps

Tracy, CA11y exp

“ML/NLP leader with 12+ years of impact across LinkedIn, TikTok, and Levi's, building and productionizing multimodal recommendation and embedding-based search systems. Deep experience in entity resolution, vector retrieval, and rigorous evaluation, with cloud-native deployment/monitoring (MLflow, Airflow, SageMaker/Lambda, Azure ML, Kubernetes) and demonstrated double-digit relevance gains at millions-of-users scale.”

Yishi Wang

Screened

Junior Machine Learning & Data Science professional specializing in LLMs and analytics

Chicago, IL3y exp

“Amazon internship experience building production GenAI analytics for the returns organization: a multi-agent LLM+RAG system that let analysts query multiple heterogeneous data sources in natural language without hand-written SQL. Also built and operationalized four Apache Airflow DAGs for large-scale ETL, emphasizing observability and freshness-aware metadata to keep outputs accurate and up to date.”

“Robotics software engineer with internship experience at John Deere and AeroVironment, working across C++/Python stacks and ROS2-based systems. Drove a proof-of-concept migration from an x86/FPGA target to NVIDIA GPU solutions and helped turn a hackathon prototype into a production-ready, CI/CD-driven build-and-deploy workflow with comprehensive automated testing.”

“ML/Simulation engineer who productionized a Multi-Agent Reinforcement Learning system for 30+ firms at Belt and Road Big Data Company, integrating research code into an enterprise backend via Dockerized deployment and scalable data pipelines on GCP/Vertex AI. Demonstrated strong production debugging by tracing apparent network timeouts to hardware memory exhaustion caused by software state-history garbage collection issues, and built custom reward functions to model complex market dynamics (entry/exit, pricing).”

“Built and deployed a production RAG-based semantic search and summarization system for large legal/technical document sets, owning the full backend (embeddings, vector store, chunking, prompting) and driving a reported 40–60% reduction in manual review time. Experienced with LangChain/LlamaIndex plus Airflow/Temporal-style orchestration, and applies rigorous evaluation/monitoring (A/B tests, drift detection, staged rollouts) to keep agentic systems reliable. Also partnered with a supply-chain manager at TE Connectivity to deliver an AI inventory recommendation tool projected to drive millions in value.”

Pranav Hariharane

Screened

Mid-Level Backend Engineer specializing in REST APIs and AWS

SF Bay Area, CA3y exp

“Backend engineer who built a new REST eligibility service at Barclays that unified siloed account logic (card/loan/deposit) and integrated with web/mobile, ultimately serving millions of users daily. Also built an end-to-end LLM-based pharmaceutical care-plan generation tool in a rapid Columbia startup competition, emphasizing configurable design, strict validation, persistence, and robust error handling.”

“Former VC and angel investor with board experience (Sand Hill Angels) who has raised money for their own companies and helped over two dozen companies prepare for fundraising and M&A. Strong fit for CEO roles in venture-backed environments given extensive capital markets and deal-prep exposure.”

“Robotics software engineer with Stanford coursework and Georgia Tech research experience, focused on end-to-end autonomy for mobile manipulation and real-time planning under uncertainty. Built a ROS 2 LoCoBot system combining Gemini speech-to-text, YOLO-based RGB-D perception, navigation, and grasping with robust synchronization/TF fixes, and developed an information-theoretic UGV planner for radiological source localization validated via Monte Carlo simulation.”

“Robotics software engineering intern at Bell Labs who overhauled indoor mobile robot localization in a ROS 2 stack, combining EKF + particle filtering with a neural network to handle BLE multipath disturbances. Delivered a major accuracy gain (~50 cm to sub-20 cm), earned a company Innovation award, published a paper, and saw the approach adopted across the company’s robot fleet.”

John Bralich

Screened

Senior Machine Learning Scientist specializing in generative AI and applied NLP

Juno Beach, FL9y exp

“ML/AI tech lead who shipped a production LLM workflow at GoDaddy for personalized marketing content, using rich customer context and human-plus-LLM evaluation to drive a statistically significant increase in customers creating posts with GoDaddy tools. Also has experience translating embedding research into a production government RFP search engine, with hands-on optimization of retrieval latency, model size, and deployment reliability.”