Vetted Vector Search Professionals

Piyush Rajendra

Screened

Mid-level AI/ML Engineer specializing in production RAG systems and MLOps

Athens, GA4y exp

“Built and deployed a GPT-4 + Pinecone RAG system that lets users query large internal document collections with grounded, cited answers. Demonstrates strong applied LLM engineering (chunking experiments, hallucination controls, metadata recency boosting) plus production-minded evaluation/monitoring and performance tuning (rate-limit mitigation via pooling/batching). Also effective at translating complex AI concepts to non-technical stakeholders through prototypes and live demos, helping secure client sponsorship.”

Varun Kothapalli

Screened

Mid-level AI/Machine Learning Engineer specializing in Generative AI, NLP, and MLOps

Saint Louis, MO6y exp

“Built a production LLM/RAG document analysis system for large financial documents (credit reports/PDFs) to help business analysts extract insights faster. Implemented end-to-end pipeline orchestration with LangChain, vector search (e.g., FAISS), and hallucination controls (context grounding, similarity thresholds, and no-answer fallback), delivered as a Dockerized Python API.”

“Backend engineer with hands-on ownership of a production POS platform, including architecture, CI/CD, Kubernetes deployment, and live incident handling. Also built a RAG-based document Q&A system using OpenAI/Anthropic with hybrid retrieval, evaluation metrics, and fallback logic, showing both traditional backend depth and practical applied AI experience.”

“Senior product leader who helped transform Submittable from a single-program grant tool into a multi-program impact platform, driving ARR from $20M to $70M+ while improving retention and margins. Particularly strong in enterprise platform strategy and human-centered AI, with a clear philosophy of using AI to augment expert judgment rather than replace it.”

“Built and shipped a production LLM-driven data harmonization/record-matching pipeline for pharmaceutical datasets, combining normalization, embeddings/vector search, and an LLM validation step. Emphasizes production reliability via guardrails, confidence thresholds, idempotent/retryable stages, and human-in-the-loop fallbacks, with monitoring focused on manual review and error rates to reduce false positives.”

“LLM solutions practitioner with SOC/alert-triage experience who takes LLM prototypes to production using RAG (Pinecone), FastAPI services, guardrails, CI/CD, monitoring, and robust fallback logic. Known for rapid real-time debugging of embedding/vector and agent workflow issues, and for driving adoption through code-first workshops and sales-aligned custom demos with measurable improvements (35% faster triage; 40% increase in correct tool usage).”

Sachin Dulla

Screened

Mid-level AI/ML Engineer specializing in NLP, fraud detection, and MLOps

Kentwood, MI3y exp

“Built and deployed a domain-specific LLM chatbot for research/support, cutting manual effort by ~50%. Demonstrates strong applied LLM engineering: RAG, prompt grounding with citations and fallbacks, embedding/top-k tuning, and production monitoring (confidence, latency, feedback loops). Experienced orchestrating agent workflows with LangChain-style pipelines and continuous evaluation to maintain reliability.”

Shrinivas Bhusannavar

Screened

Mid-level AI Engineer specializing in agentic LLM systems and RAG platforms

San Jose, CA5y exp

“Built and shipped Serrano AI, a multi-tenant SaaS conversational AI platform that automates Odoo ERP workflows and lets ops/finance/supply-chain teams query ERP data in natural language. Implemented a multi-agent architecture (LangChain/LangGraph/CrewAI) with hybrid RAG over ERP schemas, deployed on Heroku/Vercel with production observability, cutting reporting time by ~80% while addressing hallucinations, latency, and schema complexity.”

“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.”

Scott Whitlock

Screened

Director-level Product Manager specializing in Healthcare IT platforms

Cincinnati, OH23y exp

“Healthcare product leader at PatientPoint with cross-functional experience spanning design, team leadership, product integration, and product management. They’ve led content platform transformation work that reduced operational effort by 25%, improved personalized patient content delivery, and designed regulatory-conscious sponsorship and AI validation workflows for medically reviewed content.”

Anirudh Raj

Screened

Entry-Level Machine Learning & Cloud Engineer specializing in AI data pipelines

Boston, MA

“Early-career cloud/appsec-focused engineer with hands-on experience building secure, observable microservice systems on AWS (IAM least privilege, KMS encryption, Secrets Manager, CloudWatch, ALB) and troubleshooting autoscaling-related 500s down to connection pooling issues. Also deployed heavy ML workloads on Kubernetes by decomposing diffusion/transformer services, using workload identity to eliminate static credentials, and maintaining GitOps-style deployment audit trails.”