Vetted Prompt Engineering Professionals

“Built a production LLM fine-tuning pipeline for domain-specific code generation at Pigeonbyte Technologies, including automated collection and rigorous quality filtering of 10M+ code samples (AST validation, sandbox execution/testing, deduplication, drift monitoring, and human-in-the-loop review). Also implemented end-to-end ML orchestration in Apache Airflow with data quality gates, dataset versioning in S3, benchmarking, and automated model promotion, and has a reliability-first approach to agent/workflow design.”

“Built and productionized an LLM-based system that summarizes large volumes of unstructured content (customer feedback/internal docs) to reduce manual analysis and surface decision-ready insights. Brings strong reliability practices—prompt/schema constraints, validation checks, orchestration with Airflow/Databricks, and rigorous component + end-to-end testing—plus experience partnering closely with business stakeholders to drive adoption.”

“Outbound-heavy business development and demand gen operator who built scalable, trackable partner-led acquisition in Saudi Arabia—combining physical placements (fuel stations, schools/universities) with QR-to-landing-page funnels and Salesforce attribution. Has negotiated partner contracts with commission structures and driven measurable lead volume (5,000+ qualified leads; thousands tracked via partner channels) while iterating to reduce CPL using testing and keyword/tag refinement.”

“AI/backend engineer who has built and scaled production LLM-powered SaaS features (document assistant + compliance review agent) on a Node.js/TypeScript + Postgres/Redis stack deployed to GCP Kubernetes. Demonstrates strong production reliability chops—async queueing, autoscaling, observability, and database tuning—with quantified wins (p95 latency -60%, query 4s to <200ms) and robust AI guardrails (strict RAG, schema validation, citations, HITL).”

“Built and deployed an LLM-powered automation pipeline that ingests voice and documents, transcribes/extracts key information into structured data, and routes it through backend workflows using Python/FastAPI. Uses n8n to orchestrate multi-step AI processes with validation, retries, and monitoring, and iterates with stakeholders via rapid demos to refine changing requirements.”

“Built and deployed a production LLM-powered recruitment chatbot that automates key recruiting steps (sourcing, candidate engagement, screening). Strong in agent orchestration with LangGraph, including guided graph-based workflows, context-aware routing, and reliability measures like clarifying steps plus human-in-the-loop evaluation.”

“Full-stack engineer working on a medical practice SaaS, who owned an end-to-end charge entry module handling patient charges/payment workflows and complex client-side data transformations. Also built a fintech rating platform with real-time WebSocket streaming and TradingView-based charting, and has 10 years of university experience mentoring student "startup" capstone projects under tight deadlines.”

“Frontend-focused engineer who led end-to-end React/TypeScript builds for internal university systems and a complex news-email automation dashboard, emphasizing scalable architecture, RBAC/JWT auth, and robust multi-filter state management. Also improved an existing hackathon app via Redux refactors, lazy loading, responsive/SEO enhancements, and performance gains to 90+ while collaborating closely with design (Figma) and backend teams.”

“AI/ML practitioner with production experience building a RAG-powered contextual customer support agent, optimizing for low latency using vector databases and smaller LLMs. Also deployed a fraud detection model on Kubernetes with auto-scaling for heavy transactional loads, and improved chatbot accuracy by 15% through metric-driven testing and evaluation. Partners with Marketing on personalization/recommendation initiatives with measurable outcomes tied to customer feedback.”

“QA/automation tester with experience across Cypress/Playwright/Cucumber and API testing who helped a large team with limited QA bandwidth by automating a previously manual Excel verification workflow using pandas. Reported a critical data-leakage defect and found ~40 issues during an old-to-new UI migration, collaborating regularly with developers/designers to improve product quality.”