Vetted Retrieval-Augmented Generation Professionals

“Built and deployed a production LLM-powered support assistant for customer support agents using a RAG architecture over internal docs and past tickets, with human-in-the-loop review. Demonstrates strong applied LLM engineering focused on real-world constraints (hallucinations, latency, cost) using routing to smaller models, reranking, caching, and rigorous evaluation/monitoring (offline eval sets, A/B tests, KPI tracking).”

“Software engineer (Yellow.ai) who built and productionized an AI-driven resume tailoring system using embeddings + Chroma RAG + QLoRA fine-tuning, deployed via Docker/Kubernetes with CI/CD on a CPU-only Oracle VM. Demonstrates strong reliability/evaluation rigor (custom hallucination/coverage/relevance metrics) and measurable business impact, including a 60% user satisfaction lift from improving chatbot intent accuracy with product and support teams.”

“Built and deployed a production enterprise LLM-powered RAG assistant for the construction domain, enabling natural-language querying across PDFs/reports and structured sources (SQL/CSV). Implemented an agent-based routing and multi-agent orchestration approach (LangChain/LangGraph) to reduce hallucinations, improve latency, and deliver actionable, structured responses based on stakeholder feedback.”

“Internship project at Builder Market: built an end-to-end production multimodal LLM application that estimates renovation/replacement costs from appliance photos (CLIP embeddings) or text descriptions, combining fine-tuning with agentic RAG. Focused heavily on real-world performance constraints—latency and cost—using parallel agent workflows, model routing to smaller/open-source models, re-ranking, and retrieval chunking, and collaborated closely with CEO/co-founders to deliver the solution.”

“AI/LLM engineer who built a production RAG-based Text2SQL engine using Qdrant, including creating the underlying business/DB documentation, generating a test dataset, and designing detailed SQL-quality metrics for validation. Also partnered with non-technical stakeholders on a speech recognition project to prioritize medical terminology, improving accuracy through targeted corpora, lookup-table correction, and fine-tuning with a modified loss function.”

“Built and deployed a RAG-based student/faculty support chatbot at a university that answers from official syllabus/policy documents and now supports 4,000+ students while reducing repetitive support requests. Hands-on with LangChain, LangGraph, and CrewAI to orchestrate reliable agentic workflows, with a strong focus on testing/monitoring in production and cross-functional delivery (e.g., marketing analytics automation at Steve Madden).”

“Built and deployed a production RAG system for financial/compliance teams using GPT-4, Claude, and local models to retrieve and summarize thousands of internal documents with strong security controls (role-based retrieval, PII masking). Drove significant operational gains (30+ hours/week saved, ~35% productivity lift, ~45% faster responses) and orchestrated end-to-end ingestion/embedding/index refresh pipelines with Airflow, S3, and SageMaker while partnering closely with compliance stakeholders on auditability and traceability.”

“ML/LLM engineer with production experience building a RAG-based LLM support assistant (FastAPI, Redis, Kafka) with multi-layer validation and human-in-the-loop feedback loops to improve accuracy over time. Has orchestration and MLOps depth using Airflow and Kubeflow on Kubernetes (autoscaling, alerting, monitoring) and delivered measurable ops impact (40% ticket efficiency improvement) by partnering closely with customer support teams.”

“Built and deployed an AI agent to help patients navigate complex housing information by scraping and normalizing unstructured data across all 50 U.S. states, then layering a LangChain RAG system with MMR re-ranking to reduce hallucinations. Experienced in orchestrating multi-agent workflows (LangGraph/CrewAI) and production reliability practices (Pydantic-validated outputs, LLM-as-judge evals, tracing). Also delivered stakeholder-facing explainability via SHAP dashboards for a loan-approval predictive model at Welspot.”

“Robotics/perception engineer focused on production-grade, real-time systems—optimized self-supervised segmentation on Jetson Nano from ~6–10 FPS to ~20–25 FPS and scaled experimentation/deployment by unifying 15+ edge models in a modular PyTorch Lightning framework. Experienced integrating distributed LiDAR-camera fusion via gRPC/protobuf into mission planning, migrating ROS1→ROS2 Foxy for multi-drone perception, and adding Prometheus-based observability for long-running deployments.”

“Built and deployed NeuroDoc, a production-grade RAG system for PDF Q&A that delivers citation-backed answers with strong anti-hallucination guardrails. Experienced in orchestrating and scaling ML/LLM pipelines with Kubernetes, Airflow/Prefect, and PyTorch Distributed, and in building rigorous evaluation and citation-verification tooling to ensure reliability in production.”

“Software engineer with connected-car/automotive production experience who owned an end-to-end remote door lock/unlock feature and introduced unit testing (GTest) plus rig/simulator validation. Also built and productionized an AI-native AWS cloud cost assistant (Lex + GPT-based LLM + Lambda + RAG/vector DB) with guardrails and achieved 94% evaluation accuracy. Helped replace a third-party solution with an in-house build, saving the company ~€9M.”

“Built and shipped an AI-powered driver ranking/assignment system at AffirmoAI using LLM intent classification + RAG over pgvector/Postgres, served via FastAPI with a React UI that explains scores. Drove measurable improvements through optimization and iteration (latency down to <800ms, adoption 60%→90%+) and implemented rigorous eval loops with dispatcher ground truth plus cold-start handling for new drivers.”

Yashi Agarwal

Screened

Mid-level Machine Learning Engineer specializing in NLP, Generative AI, and RAG systems

Los Angeles, CA4y exp

“Built and deployed a production LLM-powered phone assistant for a healthcare clinic, combining streaming STT/TTS with RAG over approved clinic documents and strict safety guardrails to prevent unverified medical advice, plus seamless human handoff. Also has hands-on Apache Airflow experience building robust daily ML/data pipelines with data validation, retries/timeouts, monitoring, and metric-gated model deployment, and iterates closely with clinic staff using real call reviews.”

Abhishek Gupta

Screened

Mid-level Full-Stack Developer specializing in AI automation and RAG pipelines

Toronto, ON6y exp

“Frontend engineer who has led mobile-first and web React/TypeScript products end-to-end, including an expense tracking app handling sensitive financial data and a real-time messaging/activity dashboard with chat, presence, and contextual side panels. Emphasizes scalable architecture, rigorous component-boundary testing, and production-safe rollout practices (feature flags, analytics/logging, staged releases) to ship reliably in fast-paced environments.”

Teja Babu Mandaloju

Screened

Mid-level Data Scientist/MLOps Engineer specializing in NLP, GenAI, and cloud ML platforms

Chicago, USA5y exp

“AI/ML engineer who led production deployment of a multimodal (text/video/image) RAG system on GCP using Gemini 2.5 + Vertex AI Vector Search, scaling to 10M+ documents with sub-second latency and +40% retrieval accuracy. Strong MLOps/orchestration background (Kubernetes, CI/CD, Airflow, MLflow) with proven impact on reliability (75% fewer incidents) and deployment speed (92% faster), plus experience delivering explainable ML (XGBoost + SHAP + Tableau) to non-technical retail stakeholders.”

Harshini Jonnala

Screened

Senior Backend Software Engineer specializing in distributed systems and cloud microservices

Hyderabad, India2y exp

“Backend engineer with NTT Data experience building Java/Spring Boot services for product-data ingestion, including Kafka-based asynchronous pipelines and Redis read-through caching. Also built a personal RAG system deployed on Google Kubernetes Service using FastAPI, LangChain, and Pinecone with multi-tenant data isolation; holds a Master’s background in Machine Learning.”

Bhavana Anna

Screened

Mid-level AI/ML Engineer specializing in fraud detection and Generative AI (RAG)

USA5y exp

“AI/ML engineer who has shipped production LLM and ML systems, including a RAG pipeline that ingested ~500k insurance/client documents to help adjusters answer questions faster and more consistently. Experienced in handling messy real-world document formats, tuning retrieval/chunking, and reducing latency via vector search optimization, precomputed embeddings, and caching. Also built orchestrated fraud-detection deployment workflows using AWS Step Functions and SageMaker, and partners closely with non-technical operations teams on NLP automation.”

Rahul Ganesan

Screened

Intern AI Engineer specializing in LLM systems, RAG, and cloud data pipelines

Washington, PA0y exp

“Built and deployed a production Dockerized multimodal (voice+text) LLM agent for knowledge management that retrieves from Notion and documents and falls back to Tavily-powered web search with citations when internal notes are missing. Emphasizes production reliability via model-switching fallbacks, caching, strict structured outputs (Pydantic/JSON schema), and MCP-based orchestration with state-aware gating and monitoring to reduce redundant tool calls and improve success rates.”

Sai Bandaru

Screened

Mid-level Machine Learning Engineer specializing in fraud detection and LLM systems

Boston, MA6y exp

“At FiVerity, built and deployed a production LLM/RAG-based Information Gathering Tool for credit union fraud analysts that generates auditable investigation summaries from verified evidence. Focused on high-stakes constraints—hallucination prevention, cross-entity leakage controls, compliance/PII-safe monitoring, and latency—while also shipping customer-facing agentic workflows using CrewAI and LangGraph in close partnership with fraud and compliance stakeholders.”

Uttam Kumar

Screened

Intern AI Engineer specializing in LLM agents, RAG, and scalable cloud deployment

Atlanta, GA2y exp

“AI/LLM engineer at GPT integrators who built a production multi-agent enterprise workflow integration system, tackling hard problems in agent orchestration, layered memory, and custom RAG over enterprise/user data. Also built an education-focused agent solution integrating with Canvas, Zoom, and email to automate classroom admin tasks, and is currently applying agentic AI to insurance underwriting workflows in collaboration with underwriters.”

Sai Leela Kuragayala

Screened

Mid-level Full-Stack Software Engineer specializing in scalable web apps and automation

Los Angeles, CA5y exp

“UE5 UI engineer who has shipped production-ready HUD/menu frameworks using C++/Slate/UMG and CommonUI, emphasizing MVVM-style architecture for maintainability and designer-friendly iteration. Strong in UI profiling/optimization (Unreal Insights + Slate Profiler), including Slate list virtualization and event-driven updates that improved UI frame time by ~30% in heavy menu scenarios.”

“Built and shipped production AI features for Aiden, including a natural-language agent and a Knowledge Hub ingestion/retrieval system. Stands out for hands-on debugging of real LLM production issues across providers like OpenAI and AWS Bedrock, improving reliability and achieving 90% response/retrieval consistency through direct LiteLLM integration, validation, monitoring, and async system design.”

“AI/full-stack engineer who has built zero-to-one internal products around LLMs, RAG, and NLP pipelines, including a conversational data interface and a production AI agent system. Stands out for combining frontend UX for non-technical users with backend/cloud architecture and measurable impact, including a reported 60% reduction in data retrieval time.”