Vetted Prompt Engineering Professionals

Guillermo Salas

Screened ReferencesStrong rec.

Executive software engineering leader specializing in AI-augmented Healthcare SaaS platforms

Elm Grove, WI15y exp

“VP-level software engineering leader in a private-equity-backed company, overseeing a 50+ person org through aggressive growth and operational efficiency goals. Particularly strong in building the operating system around engineering—product-to-engineering governance, AI-augmented SDLC practices, ADRs, and feature-flag-driven delivery—to reduce ambiguity, dependency on institutional knowledge, and cycle time.”

Manasa Pantra

Screened ReferencesStrong rec.

Junior Software Engineer specializing in AI, LLM systems, and full-stack development

Stony Brook, NY2y exp

“Product-focused full-stack engineer at startup (Zippy) who shipped a production multi-agent AI system for restaurant operations plus payments workflows. Built end-to-end: RAG grounded on a Notion knowledge base, structured function-calling task routing, FastAPI/JWT multi-tenant backend, and a polished React+TypeScript owner dashboard. Has real production incident experience (duplicate Stripe webhooks) and reports ~94% task-routing accuracy under load.”

“Interned at Larsen & Toubro where they built and deployed an agentic RAG document question-answering system to reduce time spent searching documents and improve trustworthiness. Implemented ReAct-style multi-step orchestration with LangChain/LlamaIndex plus evidence-bounded generation, grounding/citations, and rigorous evaluation—cutting latency ~40%, hallucinations ~35%, and unsafe outputs ~40% while collaborating closely with non-technical business/ops stakeholders.”

“Co-founder and employee #1 who built Reva, a vertically integrated conversational CRM for multifamily leasing and resident services, leading product, engineering, and design from inception through acquisition. Combines deep enterprise product leadership with early AI experience, including a pre-LLM chatbot that drove a 400% lift in lead conversion and prior edtech work where AI-enabled learning tools improved struggling students by a full letter grade on average.”

“AI/ML infrastructure-focused engineer who has built production RAG systems from scratch (Supabase/pgvector + OpenAI embeddings) and iterated using formal eval metrics to improve retrieval quality. Also debugged real-time audio issues in a LiveKit-based pipeline by correlating packet loss with VAD behavior, and has deep experience building brittle, customer-specific financial platform integrations in Python/Playwright (2FA, redirects, token refresh, rate limits).”

“Built and deployed a GPT-4-powered medical assistant for clinical staff to reduce time spent searching guidelines and EHR information, with a strong emphasis on safety and compliance. Uses strict RAG, confidence thresholds, and fallback behaviors to prevent hallucinations, and runs production-grade workflows orchestrated with LangChain/LangGraph plus Docker/Kubernetes/MLflow and monitoring for reliability and cost.”

“LLM/ML engineer who has shipped an enterprise RAG-based Q&A system (LangChain/LlamaIndex, FAISS + Azure Cognitive Search, GPT-3.5/4 via OpenAI/Azure OpenAI) to production on Docker + Kubernetes/OpenShift, tackling hallucinations, retrieval quality, latency/cost, and RBAC/IAM security. Also partnered with operations leaders to turn manual reporting into an LLM-powered summarization and forecasting dashboard driven by real KPIs and iterative stakeholder feedback.”

“LLM/agentic systems practitioner focused on taking customer prototypes to production by hardening reliability (APIs, monitoring, security) and adding guardrails, evals, and incremental rollouts. Experienced diagnosing RAG/agent failures via structured tracing and fixing retrieval-quality issues (freshness checks, filters, schema enforcement). Also supports pre-sales by leading developer demos/workshops and building targeted POCs to address scalability/reliability objections and drive adoption.”

“Growth creative lead with hands-on experience running end-to-end paid creative tests for major music/entertainment campaigns at UMG, including a vinyl sale push that cut CPA by 20%+ in a tight promo window. Known for a modular ad-building approach (hooks/value prop/proof/CTA) and platform-native optimization across Meta, TikTok, and YouTube, plus strong label/creative-team alignment and QA discipline (including tracking).”

“Built a production RAG-based “unified assistant” that consolidates siloed company documents into a single chatbot while enforcing fine-grained access control via RBAC/metadata filtering with OAuth2/JWT. Experienced orchestrating LLM workflows with LangChain/LangGraph + FastAPI (async + caching) and measuring performance via retrieval accuracy and response-time SLAs. Also delivered a churn analytics solution with dashboards and automated retention campaigns using n8n.”

“Built and deployed a production RAG-based document Q&A system on Azure OpenAI to help business teams search thousands of PDFs/Word files, using Qdrant vector search, MongoDB, and a Flask API. Demonstrates strong production engineering (streaming large-file ingestion, parallel preprocessing, monitoring/retries) plus systematic prompt/embedding/chunking experimentation to improve accuracy and reduce hallucinations, and has hands-on orchestration experience with ADF/Airflow/Databricks/Synapse.”

“Built and deployed a production LLM system that scraped Google Maps menu photos, extracted structured prices via OpenAI, and cross-validated them against website-scraped data to automate data-quality verification at scale (replacing costly manual contractor checks). Demonstrates strong reliability instincts—precision-first prompting, output gating with image-quality metadata, and fuzzy matching/RAG techniques—plus solid orchestration (Dagster/Airflow) and observability (Sentry, Prometheus/Grafana).”

“ML/NLP engineer who built a RAG-based technical assistant for Caterpillar field engineers, transforming PDF keyword search into intent-based semantic retrieval across manuals, logs, sensor reports, and technician notes. Strong in productionizing data/ML systems (Airflow, PySpark) with rigorous preprocessing, entity resolution, and evaluation—delivering measurable gains in accuracy, relevance, and duplicate reduction.”

“Backend/AI engineer who built a Spring Boot timesheet API on AWS (Postgres, Docker, Nginx) used by hundreds of daily users and resolved severe deadline-driven latency/5XX incidents via query optimization, connection pool tuning, and Redis caching. Also shipped application-layer LLM features (Mistral + LangChain chatbot) and designed a Planner/Executor/Verifier troubleshooting agent with verification-based guardrails to prevent hallucinated root-cause analyses.”

“Backend engineer with recent Barclays experience building a Python asyncio + Kafka risk reporting service for trading desks, including a major refactor from blocking batch processing to event-driven incremental pipelines to restore intraday/EOD performance. Also shipped an applied AI feature using OpenAI fine-tuning to classify risk-breach severity and generate trader/risk-manager summaries with robust retry/fallback handling, plus demonstrated strong database/query optimization (triggers, materialized views, partial indexes) in a risk-limits/breaches domain.”

“Deloitte engineer who built and shipped AI-powered, Kafka-driven workflow automation for transportation/document processing, including LLM-based semantic search. Strong in production reliability (idempotency, offset management, retries), observability (Datadog/CloudWatch), and database performance tuning (PostgreSQL/Flyway), with measurable latency improvements.”

“Currently at Huntington Bank, built a production-grade RAG system that helps business/operations teams get grounded answers from large volumes of internal enterprise documents. Owns ingestion and FastAPI backend, tuned hybrid BM25+vector retrieval and chunking for relevance, and evaluates reliability with metrics and observability (LangSmith, CloudWatch, Prometheus/Grafana) while partnering closely with non-technical stakeholders.”

“QA automation engineer who owned and transformed a JavaScript/WebdriverIO UI automation suite into a reliable CI release gate by refactoring to POM, adding API validations, cross-browser coverage, and aggressively reducing flakiness. Demonstrated strong security mindset by uncovering a critical RBAC gap where restricted users could trigger billing actions via direct API calls, and helped reshape product requirements early through detailed acceptance-criteria questions (e.g., CSV upload partial failures).”

“Internship at ActiveVM where they tackled large-scale Spring Boot 2→3/library migrations across hundreds of downstream products by combining OpenRewrite (AST-based recipes) with an LLM/RAG-based classifier that routed risky files to human experts. Reported ~70% reduction in manual effort and 90%+ accuracy after testing across multiple branches and cutovers; also built a CTR-driven book recommendation capstone showcased at the Google office in Cambridge.”

“Software engineer with experience across TCS, Rakuten, and USC who has owned production integrations and data pipelines end-to-end. Notably improved a trading platform payment flow by replacing fragile polling with a webhook-driven status system with robust fallbacks, and has shipped LLM-assisted design-to-webpage automation plus evaluation-driven prompt iteration (NYT Connections).”

“Enterprise/technical seller with ~2 decades of complex sales experience, focused on high-ACV AI/robotics-style deals. Self-sourced and closed a robotics opportunity into a $500K paid pilot that expanded to $1M, coordinating NDAs and multi-party integrations across NVIDIA GPU/CUDA, camera, and VMS stakeholders. Also founded a startup and built early pipeline from scratch, leading a small sales team to close initial deals in a pre-revenue MVP stage.”

“Engineer with production experience in both high-throughput banking risk systems and LLM agent platforms. Built a real-time transaction risk scoring middleware at JPMorgan Chase (1M+ requests/day) emphasizing HA, observability, and audit/PII compliance, and also architected multi-step LLM agents with strict schema-based tool calling, evaluation loops, and safety guardrails for messy enterprise data.”

Cristian Vega

Screened

Senior AI/ML Engineer specializing in Generative AI and RAG

California, null9y exp

“ML/NLP practitioner at Morf Health focused on unifying fragmented healthcare data by linking structured patient/encounter records with unstructured clinical notes. Has hands-on experience with transformer embeddings, vector databases, and domain fine-tuning, plus rigorous evaluation (precision/recall) and human-in-the-loop validation with clinical SMEs to make pipelines production-grade.”

HIMANSHU SHARMA

Screened

Mid-level AI Solutions Engineer specializing in enterprise GenAI and automation

Orlando, FL6y exp

“Built and shipped multiple production LLM/agentic systems, including an agentic RAG NL-to-SQL analytics app that cut manual reporting from 9 hours/week to 15 minutes by grounding on schema-aware retrieval and robust fallback/monitoring. Also implemented a LangChain supervisor-orchestrated enterprise IT automation agent that routes requests for search, identity validation, and action execution, and created a RAG search tool spanning Jira/Confluence/SharePoint for operations stakeholders.”