Vetted Recommender Systems Professionals

Junhui Huang

Screened

Intern Machine Learning Engineer specializing in LLMs, MLOps, and NLP

Providence, RI1y exp

“Built and deployed a production LLM-driven Dungeons & Dragons game where the model acts as a dungeon master, adding a structured combat system and a macro-state tree to ensure campaigns converge to a clear ending. Fine-tuned Gemini 2.5 Flash on Vertex AI and deployed on GCP with Kubernetes, using RAG over DnD rules/spells plus multi-agent orchestration (intent-based routing between narrative and combat agents) to reduce hallucinations and improve reliability.”

Vaibhav Sharma

Screened

Mid-level Software Engineer specializing in AI/ML and data platforms

Remote, USA5y exp

“AI/ML engineer who built a production agentic system to automate computational research experiments (simulation execution, parameter exploration, and numerical analysis) and mitigated context-window failures using constrained tool-calling/prompt-chaining patterns in LangChain with OpenAI tool-enabled models. Also has adtech/big-data pipeline experience at InMobi, orchestrating Spark jobs in Airflow to filter bot-like user IDs and publish clean IDs to an online NoSQL store for live serving, plus Apache open-source collaboration experience.”

“Product-minded full-stack engineer with deep experience building React/TypeScript + serverless/SQL systems and improving both operational reliability and user activation. They stand out for connecting low-level architecture decisions like idempotency and atomic updates with business outcomes such as reduced support burden and lower 30-day cancellations, while also designing configurable multi-tenant product systems with strong guardrails.”

Amit Dharam

Screened

Junior AI/ML Software Engineer specializing in backend systems and cloud deployment

Tempe, AZ3y exp

“Built multiple end-to-end automation and data systems, including an Accio RAG pipeline combining PDF parsing, FastAPI, Neo4j, and vector search, plus Selenium-based scraping for a virtual try-on product. Stands out for reliability-minded engineering: automated testing, structured logging, validation layers, and a data-driven approach to debugging flaky automation that improved CI pass rates to over 98%.”

“AI/LLM engineer with strong healthcare domain depth who has shipped production-grade agents for care coordination and clinical workflow automation. Stands out for combining Knowledge Graph RAG, LangGraph orchestration, and rigorous eval/guardrail systems to improve reliability in high-stakes environments, with measurable gains in review time, hallucination reduction, latency, and clinician adoption.”

“ML/AI engineer with hands-on ownership of a production LLM/RAG system at Goldman Sachs, focused on workflow automation and large-scale document search for operational teams. They combine strong MLOps and backend engineering skills with practical GenAI evaluation and safety practices, and cite measurable impact including 22% better task guidance accuracy and sub-second search across millions of records.”

“Product-minded full-stack engineer with experience shipping engagement features and core communication systems at DribbleUp and Expys. Stands out for combining rapid MVP execution with rigorous iteration: delivered a leaderboard feature that lifted engagement by 8% initially and 20% overall, built a chat MVP in 3 days, and has hands-on experience deploying LangChain-based concierge agents with evals and human review.”

“ML/NLP engineer with experience at Flatiron Health building a production NLP platform that processed millions of clinical notes, using BERT/BiLSTM-CRF and spaCy to extract and normalize entities from noisy EMR text with oncologist-in-the-loop validation. Also built scalable retail ML workflows (Spark + Kubernetes + feature store caching) and applied vector databases plus contrastive-learning fine-tuning to improve retrieval relevance and recommendations.”

“Built internal production LLM tools for engineering and support, including a customer-health assistant and a RAG-based incident explainer grounded in logs, metrics, and deploy data. Stands out for combining strong GenAI safety/evaluation practices with pragmatic backend engineering, delivering measurable impact like a 40% drop in data-help requests and answers in seconds instead of minutes or hours.”

“ML/AI engineer with hands-on experience building production healthcare AI systems across predictive modeling and GenAI. They built an end-to-end patient risk prediction platform and a RAG-based clinical summarization feature, combining strong NLP/LLM skills with AWS deployment, monitoring, drift detection, and reusable Python service design to deliver measurable clinical and operational impact.”

Yuchen Shen

Screened

Junior Full-Stack Engineer specializing in AI and graph-based applications

Sunnyvale, CA2y exp

“Front-end engineer with experience at Walmart and Sam's Club building sophisticated browser-based UIs, including an internal AI chat experience that combined chat, document rendering, editing, and visualization in one workflow. Stands out for pairing modular React/TypeScript architecture with hands-on browser performance debugging and Apollo/GraphQL optimization for highly interactive enterprise interfaces.”

Scott Mangino

Screened

Executive product leader specializing in SaaS and consumer mobile platforms

Denver, CO14y exp

“Product leader who blends strategy, UX, and hands-on building—designing in Figma, using Claude Code to prototype and push PRs, and partnering closely with engineering to ship faster. Led the Travel Noire app from content brand extension to AI-enabled travel companion, and independently built human-centered AI workflows for grassroots volunteer management. Particularly strong in trust-based, assistive AI products that amplify human relationships rather than replace them.”

“Frontend/full-stack engineer currently at Intuit working on QuickBooks pricing and personalization experiences, where she is the sole developer for a production shopping widget on quickbooks.intuit.com. She combines strong React performance optimization skills with cross-functional product ownership, including integrating an ML-based SKU recommendation model and proactively shipping UX improvements like browser back-button support after winning stakeholder buy-in through a POC.”