Vetted Retrieval-Augmented Generation (RAG) Professionals

Rubeena Riyas

Screened References

Senior AI Engineer specializing in LLM agents, RAG, and scalable data platforms

7y exp

“ML/data engineer who owned an end-to-end production sales analytics pipeline at 15,000+ user scale, delivering ~50% compute reduction, ~80% faster reporting, and ~$1.2M impact. Also shipped a production RAG-based AI assistant over internal BigQuery/docs with evaluation metrics and safety guardrails, and built shared Python libraries to standardize reliability and accelerate engineering teams.”

“At PwC, built and productionized an agentic RAG enterprise search assistant over 6M internal documents (8M embeddings), deployed across AWS and GCP. Drove major retrieval gains (72%→92% precision via BM25+dense hybrid with RRF and cross-encoder re-ranking), reduced hallucinations 30%, achieved <2s latency at 50–60K queries/month, and cut support tickets 30%—boosting adoption to 2,500 users by adding source-cited answers.”

Vijay Mohan

Screened ReferencesStrong rec.

Executive technology leader specializing in AI, cloud platforms, and enterprise software

Nashua, NH25y exp

“Founder of Braindump (redfly.ai), an AI companion for memory and personal organization, who has already raised $60,000 from Alchemist Accelerator. Brings hands-on experience with customer discovery, technical validation, and VC pitching, and stands out for a first-principles approach to building products with dramatic performance and cost improvements.”

“Full-stack engineer with hands-on experience spanning real-time AI products, large-scale payments migration, internal research infrastructure, and open-source ML tooling. Particularly compelling is the mix of low-latency React/Node/TypeScript systems work, zero-downtime migration of 50,000 accounts across 12 regions, and proactive contributions to Kubeflow build and security reliability.”

Sreenaina Koujala

Screened ReferencesStrong rec.

Mid-level Full-Stack & AI Engineer specializing in cloud and intelligent systems

Ashburn, VA8y exp

“Builder with experience across government contracting, engineering automation, and solo AI product development. They architected a serverless AWS pipeline that converted unstructured BIM data into IFC 3D models, built an enterprise internal-data chatbot with auditability and guardrails at Steampunk, and independently launched an AI study platform using Claude. Strong fit for early-stage or ambiguous environments where end-to-end ownership and practical AI systems matter.”