Vetted Retrieval-Augmented Generation (RAG) Professionals

TEJASWI GUDIMETLA

Screened

Mid-level Software Engineer specializing in backend systems, microservices, and AI pipelines

North Carolina, USA4y exp

“AI/LLM engineer focused on building reliable, scalable multi-agent and RAG-based pipelines across microservices. Stands out for combining practical experimentation with strong engineering discipline around schema validation, retries, observability, and structured API contracts to make LLM systems production-ready.”

“Built a production AI stylist agent for retail store associates that pulls customer loyalty, address, and purchase-history data to generate in-store product recommendations. Demonstrates hands-on experience across agent orchestration, MCP-based tool integration, Vertex AI/GCP deployment, observability, resilience patterns, and pragmatic production tradeoffs like using PostgreSQL with pgvector instead of a standalone vector database.”

Tyler Brown

Screened

Mid-level Forward-Deployed AI Engineer specializing in LLM automation for financial services

Atlanta, GA5y exp

“Solo builder who created a real-time AI call coaching product for sales reps in just 18 days, spanning 29 stateless edge functions, 157 SQL migrations, and ~72k lines of TypeScript. They combine strong full-stack engineering with sharp product instincts, using direct user feedback and SQL-driven observability to iterate quickly and win early paying customers without marketing.”

Shreyas Kumar

Screened

Entry-level AI Engineer specializing in LLMs and applied NLP systems

Syracuse, NY1y exp

“Built Lumo, a real-time voice AI companion, owning the product end-to-end across React/TypeScript, FastAPI WebSockets, and PostgreSQL. Stands out for combining deep full-stack systems thinking with voice UX polish, reliability instrumentation, and configurable parent-control guardrails in a multi-tenant setup.”

SUMANTH Metimath

Screened

Mid-level Data Scientist specializing in ML, LLMs, and AI systems

Potsdam, NY3y exp

“Candidate takes a pragmatic approach to AI-driven development, using AI as a productivity and learning aid while emphasizing personal understanding and code validation. They have hands-on experience applying AI-assisted workflows to a resume analysis and ATS scoring project, including code generation, debugging, parsing logic improvement, and testing.”

“Full-stack engineer who has owned an operations/reporting dashboard end-to-end, spanning React/TypeScript frontend architecture, ASP.NET Core APIs, and SQL data access. Stands out for combining strong UI performance optimization with pragmatic backend decisions, post-launch monitoring, and 0→1 startup platform building that improved API speed by 35% while supporting 2,000+ transactions per hour.”

Raj Shrinivasan

Screened

Principal AI Engineer specializing in agentic systems and cloud-native platforms

Cleveland, OH23y exp

“Built a production RAG-powered analytics copilot at Aya Healthcare for operations leaders and analysts on a large healthcare staffing platform processing over a billion telemetry records annually. Stands out for strong production-minded agent engineering: deterministic orchestration, grounding-first design, deep observability, and data-driven workflow changes such as confidence-based human review for a PR review agent.”

“Built an AI-driven trading mentor/analytics system combining a React frontend, Spring Boot backend, and FastAPI ML service for stock risk and indicator analysis. Stands out for a pragmatic approach to AI-assisted development: uses AI for acceleration, but manually reviews code line by line, validates outputs against real market behavior, and adds safeguards for unreliable financial data.”

“Full-stack engineer with a strong builder mindset who has shipped both enterprise workflow software and AI-powered assistant platforms. They combine React/TypeScript and Node.js depth with hands-on experience in LLM/RAG systems, vector search, and reusable MCP-based agent infrastructure, and have delivered customer-facing products for enterprise operations teams including 40+ features across two products.”

“Solo builder of multiple AI products including CareerCopilot and ResearchMind AI, with hands-on experience across full-stack engineering, streaming LLM UX, grounded research workflows, and conversational AI. Also brings research depth as first author of an IEEE FG2026 paper on out-of-distribution deepfake detection, plus early-stage R&D experience building real-time robotics control software during an internship at ISRO's Space Applications Centre.”

“Frontend engineer with experience building data-intensive, near real-time React/TypeScript applications in credit reporting and logistics tracking. Stands out for performance-focused architecture across dashboards and map-based products, including large dataset optimization, profiling, and Mapbox visualizations with live asset tracking.”

“Full-stack product engineer with hands-on ownership of a B2B e-commerce catalog at Mphasis, spanning React/TypeScript, Java/Spring Boot, and Postgres, with strong evidence of performance tuning and UX improvement. Stands out for tracing production issues across layers, leading an Angular-to-React migration, and pairing product instinct with rapid AI prototyping—placing 2nd in an advanced AI hackathon.”

Saketh Kota

Screened

Mid-level Data Scientist / ML Engineer specializing in Generative AI, RAG, and MLOps

Irving, TX4y exp

“Built and productionized a RAG-based LLM research assistant for biomedical and regulatory document search using Mixtral 7B on SageMaker, LangChain, and Milvus, cutting research time by ~40%. Has hands-on multi-cloud MLOps experience across AWS/Azure/GCP with Kubeflow/Airflow/Composer plus Terraform + ArgoCD, and applies rigorous evaluation/monitoring (latency, accuracy, hallucinations). Also partnered with a non-technical PM to deliver an insurance policy Q&A chatbot that reduced customer response time by 30%+.”