Vetted Embeddings Professionals

“Built and deployed an LLM-powered clinical decision support and risk monitoring platform for mental health at Valuai.io, emphasizing low-latency, evidence-grounded responses and crisis-safe behavior with clinician escalation. Strong production agent-orchestration background (LangChain/CrewAI) plus rigorous evaluation (clinician-in-the-loop + evaluator agent) and large-scale synthetic testing; also applied multi-agent workflows to document verification and fraud detection during an AI internship at Nixacom.”

“AI/back-end engineer at Canon who helped build and operate an internal production LLM platform that acts as a secure middle layer between users and models, defending against jailbreaks/prompt injection while enabling RAG, memory, and grounded responses over company data. Experienced with LangChain/LangGraph orchestration, vector DB retrieval, and reliability practices (testing, monitoring, adversarial prompts) to run high-throughput, low-latency AI workflows in production.”

“Built and deployed LLM-powered features for a startup organizational management application, focusing on real-world deployment constraints like latency and cost. Implemented RAG with FAISS and improved retrieval quality by switching embedding models (OpenAI/Hugging Face) and fine-tuning embeddings on medical corpora for a medical-report UI feature. Uses LangChain and LangGraph to orchestrate multi-node LLM API workflows and evaluates systems with metrics like latency, cost per request, and error taxonomy.”

“ML/NLP practitioner who has delivered production systems in regulated domains, including a healthcare compliance pipeline using RAG (GPT-4/Claude) plus TF-IDF retrieval that increased document review throughput 4.5x. Also has hands-on experience improving fraud detection data quality via entity resolution (Levenshtein, Dedupe.py) validated with A/B testing, and building scalable, monitored workflows with Airflow, CI/CD, and AWS SageMaker.”

“Built a LangGraph-based, tool-routing LLM chatbot to deliver fast, trustworthy investment-stock insights (including tariff impact) and deployed it to production on Snowflake after initially developing in Azure with AI Search and the Microsoft Agent Framework. Improved routing robustness by moving from LLM-based decisions to a deterministic router backed by schema-relationship graphs and YAML metadata, and ran the project iteratively with non-technical stakeholders over an 8-month engagement.”

Ronald Forte

Screened ReferencesModerate rec.

Entry-Level Software Engineer specializing in AI APIs and RAG systems

0y exp

“Junior/entry-level AI/LLM engineer who built a production-oriented RAG onboarding and knowledge assistant that ingests GitHub repos and internal sources (e.g., Confluence/Jira) using ChromaDB, with reliability features like retrieval fallbacks, retries, caching, and monitoring. Currently implementing a LangGraph-based multi-agent workflow with intent routing and Pydantic/Magentic-validated structured outputs, plus CI/CD offline evals and online metrics (Grafana/Prometheus) to improve predictability and reliability.”

Priyank Jhaveri

Screened ReferencesModerate rec.

Junior AI/ML & Mobile Engineer specializing in LLMs, synthetic data, and React Native

New York, United States1y exp

“Currently at Uplift AI shipping production LLM features that generate personalized growth insights from user reflections using BERT + embeddings + RAG, with strong safety/guardrail practices for sensitive contexts. Also built an end-to-end React Native UGC challenge submission/moderation system that improved repeat submissions and 7-day retention, and has applied rigorous clinical-style evaluation methods on a dental X-ray disease detection project to reduce false negatives.”

“Built a production-grade, semi-automated document recognition and classification system for large volumes of scanned PDFs, starting from little/no labeled data and handling highly variable scan quality. Deployed on AWS using SageMaker + Docker and orchestrated on EKS with a microservices design that scales CPU-heavy OCR separately from GPU inference, with strong reliability controls (validation, fallbacks, retries, readiness probes).”

“Built and deployed an LLM product called "Content Craft" combining BART-based summarization with a RAG Q&A chatbot using LangChain, embeddings, and a vector database. Has hands-on MLOps experience containerizing and serving models with FastAPI and running them on Kubernetes with monitoring, self-healing, and autoscaling, and has practical experience reducing hallucinations through structured prompting.”

“Built and deployed a production RAG-based clinical decision support assistant at MedLib, focused on fast, trustworthy answers from large medical documents. Demonstrates deep practical experience improving retrieval accuracy (semantic chunking + metadata-aware search), controlling hallucinations with grounded generation and thresholds, and adding clinician-requested citations using chunk metadata, with evaluation driven by healthcare professional review.”

Vengalarao Pachava

Screened

Junior AI Data Engineer specializing in Azure Databricks lakehouse and GenAI RAG systems

Irving, TX2y exp

“Backend/applied AI engineer from Cloud Rack Systems who built production GenAI/RAG and data platforms on Azure/Databricks at enterprise scale (2.5M records/day). Known for making LLM systems behave like deterministic services via strict retrieval contracts, citation-based validation, and strong observability—shipping a knowledge assistant used daily by 50+ users while driving hallucinations near zero and materially improving latency and cost.”

“Production-focused ML/AI engineer with hands-on ownership across classical ML and GenAI systems, from CV/NLP services to enterprise RAG. Stands out for combining research-to-production execution with measurable business impact: 40% processing-efficiency gains, 35% fewer support tickets, 5x latency improvement, and 3x throughput gains while maintaining safety and quality.”

“Built and shipped a real-time AI robotic inspection system, using a synthetic data generation pipeline to address rare edge cases—cutting data collection costs ~60% and boosting hard-scenario accuracy ~20%. Experienced in productionizing ML on constrained Jetson hardware and orchestrating end-to-end ML workflows with Airflow/Docker/Kubernetes, with a metrics-driven approach to reliability, evaluation, and stakeholder communication.”

“Data science professional who has built a production RAG-based LLM question-answering system ("Flash Query") to deliver fast, accurate answers over large document collections, focusing on retrieval quality and grounded responses. Also collaborates with non-technical retail/jewelry stakeholders to turn business questions into predictive models and dashboards for decision-making.”

Sree Manasa Vuppu

Screened

Mid-level AI Engineer specializing in Generative AI, LLMs, and RAG

Charlotte, NC5y exp

“Internship at Discovery Education building a production LLM/RAG chatbot that let marketing and sales teams query and interpret Looker/BI dashboards in natural language, with responses grounded in compliance and state education standards. Emphasizes rigorous evaluation (faithfulness/precision/recall/latency) plus user-feedback analytics, and used LangChain for orchestration, chunking/context-window control, and integration with enterprise sources like SharePoint.”

“Built and deployed real-world systems end-to-end across security and healthcare contexts: led a 3-person team delivering a university vehicle tracking system with 30% cost savings and 1-year post-launch monitoring. Also implemented a healthcare RAG chatbot with adaptive query routing that cut LLM costs by 40% while maintaining answer accuracy, and has experience debugging non-deterministic LLM behavior in DevOps pipeline automation.”

“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.”

“Built and shipped an end-to-end AI-powered portfolio chatbot, owning the React frontend, FastAPI backend, and FAISS-based retrieval layer. Demonstrates hands-on full-stack product thinking with attention to UI performance, TypeScript maintainability, and post-launch iteration on response relevance and speed.”

“Product-minded engineer who has owned features end-to-end, including a full onboarding redesign that lifted completion ~25% and a production LLM/RAG report-generation system with strong guardrails (schema-constrained JSON, confidence gating, logging) and an automated eval/regression loop built from real user queries. Also built a scalable research data pipeline ingesting messy PDFs/JSON/CSVs with normalization, idempotent reruns, observability, and cost/latency tradeoffs.”