Vetted Prompt Engineering 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.”

Alexey Elkin

Screened

Junior Full-Stack Software Engineer specializing in cloud analytics and web applications

New York, NY2y exp

“LLM/agentic workflow engineer with hands-on experience turning demo-grade LLM analytics into production-ready features by tackling tail latency, observability, and cost/reliability controls. Strong at diagnosing real-time customer incidents via trace-driven debugging across retrieval, tool calls, retries, and prompt/version metadata, and frequently partners with sales as a technical lead to de-risk enterprise pilots through transparent failure-mode walkthroughs.”

Atharva Bhide

Screened

Entry Software Engineer specializing in AI/ML and multimodal systems

Los Angeles, CA1y exp

“Built and shipped a production healthcare AI platform for a clinic in Brea, LA that combined LLM-based clinical report generation, voice agents for appointment workflows, and camera-based patient monitoring. Stands out for pairing multimodal AI architecture with production-grade reliability and compliance practices, while delivering concrete business results including 90% workflow automation, 200 hours saved per month, and a 60% improvement in customer retention.”

“Built a production Python microservice around Grafana Loki focused on reliability, with checkpointing, idempotency, replay tooling, tracing, and alerting to prevent data loss and silent lag. Also has hands-on experience hardening brittle Playwright automations against dynamic UIs, auth expiry, rate limits, MFA, and bot-detection constraints, plus turning tribal-knowledge SOPs into explicit state-machine-driven workflows.”

“Early-career analytics candidate with hands-on experience in SQL/Python data pipelines, Tableau reporting, and marketing engagement analytics across internship and startup settings. Stands out for combining rigorous data quality practices with practical AI system design, including an end-to-end GPT-4 grading capstone that emphasized explainability and human oversight.”

“Built and owned Alight's AI-powered Search Summary feature end-to-end, using a RAG pipeline with OpenSearch and Bedrock, and drove a 20% increase in user feedback scores. Stands out for bringing rigorous production evaluation to LLM systems via live LLM-as-a-judge monitoring, and for experience with advanced agentic architectures, hybrid search, and reranking at scale.”

“ML/AI practitioner with hands-on experience shipping production time-series forecasting and RAG-based customer insights platforms in an enterprise setting. At BASF, he improved seed sales forecasting beyond naive baselines using model selection tailored by brand size, and he also led a RAG solution over Salesforce reports, complaints, and surveys that reached 2,000+ users with strong daily engagement.”

Francois Odienne

Screened

Director of Engineering specializing in SaaS platforms, AI-first product development, and telecom

Moorpark, CA28y exp

“Technology leader with venture-backed company experience at LogicMonitor, where they aligned engineering strategy with business growth and long-term platform scalability. Demonstrates strong familiarity with the VC/studio/accelerator ecosystem and a thoughtful founder mindset centered on validating real problems, innovative solutions, and execution capacity.”

“AI/backend engineer who has owned production applied-ML systems end to end, including a Jitsi meeting intelligence platform with custom RoBERTa boundary detection, LLM summarization, and automated retraining from user feedback. Also has healthcare AI experience building a diabetes medication titration system with strict validation, drift monitoring, and safety guardrails—showing both product speed and high-stakes engineering rigor.”

“Backend/product-minded engineer from Ramp with strong travel-tech experience, having built an end-to-end booking platform integrating multiple external providers, policy enforcement, and reporting infrastructure. They also shipped an LLM-powered personalization workflow using embeddings and Google Gemini that cut trip planning time by 22%, and demonstrated strong production reliability instincts through circuit breakers, health checks, and schema-driven normalization.”

Rayhaan Mohamed

Screened

Intern Software Engineer specializing in AI and full-stack development

Alpharetta, GA1y exp

“Early-career software engineer with internship experience at CirrusLabs building a voice-enabled CRM workflow that integrated Google Text-to-Speech and GPT-based processing for automated deal creation. Stands out for a reliability-focused approach to AI integrations, including validation, structured logging, prompt refinement, and hardening asynchronous API/UI behavior in real-world application flows.”

“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.”

“UC Berkeley CS–trained hands-on engineering leader with executive experience spanning fundraising and board/customer communication. Led architecture and roadmap for AI-driven fintech platforms (including portfolio data, market signals, document processing, and Bitcoin trading), scaling global orgs (~100 people) and driving modular API-based designs that improved reliability, onboarding speed, and customer retention.”

Keyan Luo

Screened

Intern-level software engineer specializing in backend and AI-powered applications

Los Angeles, CA1y exp

“Built a zero-to-one AI-powered resume tailoring platform as a personal project, owning everything from user problem discovery to frontend, backend, and AI agent architecture. Particularly strong in turning complex multi-agent AI workflows into a simple product experience for non-technical users, with a practical focus on output quality, validation, and rapid MVP iteration.”

PavanKumar Varkala

Screened

Mid-level Full-Stack Engineer specializing in AI-driven web applications

Jersey City, NJ4y exp

“Built and shipped an AI-driven operational workflow platform at Adobe that handled 12k+ monthly requests using React, Node.js, TypeScript, OpenAI APIs, PostgreSQL, Redis, and RAG. Stands out for combining full-stack product ownership with production-grade LLM architecture, evals, and human-in-the-loop controls, delivering measurable gains including 38% higher accuracy and 40% less manual triage.”

“Senior/brand designer with portfolio work spanning fintech and AI SaaS, including leading the Daylight Grow launch for an LGBTQ+ audience across multiple channels (web, social, email, events, merch, OOH). Expanded Cleanlab’s Figma design system in close partnership with engineering and led visual QA to support fast, high-volume iteration without quality drift; also drove direct mail + landing page redesigns for Happy Money to improve acquisition performance.”

“LLM/RAG practitioner who productionized an LLM-driven customer communication and transaction understanding system at PayPal, emphasizing privacy/compliance guardrails and large-scale data normalization. Experienced in real-time debugging of hallucinations via retrieval pipeline tuning and in leading hands-on developer workshops and sales-aligned POCs to drive adoption.”

“Backend engineer focused on building production-ready Python services (Flask/FastAPI) with strong performance and scalability instincts—Celery/Redis background processing, robust multi-tenant isolation (Postgres RLS), and pragmatic CI/Docker operations. Demonstrated measurable DB optimization impact (cut a critical analytics query from ~1–2s to ~100–150ms) and has hands-on experience integrating LLM/ML workflows (OpenAI, LangChain, embeddings, Redis/FAISS vector stores) without degrading API responsiveness.”

“Completed an Amazon externship building a GPT-4 + RAG pipeline to summarize themes from hundreds of employee reviews for workforce analytics aimed at improving warehouse retention. Emphasizes production-readiness through labeled-data evaluation, source attribution for explainability, human-in-the-loop review, and rigorous data cleaning/observability to debug real-world LLM workflow issues.”

“Software engineer with strong end-to-end ownership across frontend, backend, data, and infrastructure, including real-time systems (Kafka/Postgres) and observability (Datadog). Built and productionized an AI-native RAG support assistant (OpenAI embeddings + Pinecone) with prompt/guardrail design, achieving 48% agent adoption and 30% faster responses. Experienced in legacy modernization and reliability work using feature flags, event/transaction replay, and rapid embedded delivery.”

“Built GoEngineer’s first production AI systems, including an end-to-end RAG pipeline for SolidWorks technical support using Azure Blob Storage, Azure AI Search, and Azure OpenAI, plus an AI summarization feature adopted by sales/customer success. Strong in productionizing LLM workflows with evaluation harnesses (golden sets, LLM-as-judge, red teaming, shadow deploys) and Azure infrastructure integrations (Redis, Service Bus, App Insights), and has also implemented a custom MCP server for agentic monitoring.”

“Built and productionized an internal LLM-powered document Q&A system at Morgan Stanley using a LangChain-based RAG pipeline (FAISS + OpenAI) with AWS ingestion (S3/Lambda), handling 100k+ pages and cutting lookup time ~35% while keeping responses under 3 seconds. Strong on reliability: automated evals/CI (pytest + GitHub Actions), CloudWatch monitoring, drift detection (prompt drift and fraud-model drift), and security controls (IAM + app-level authorization) in a financial-services environment.”