Vetted Prompt Engineering Professionals

“New grad software engineer with hands-on experience building production full-stack analytics infrastructure during a Swiftwise AI internship and independently shipping AI products. Stands out for combining strong TypeScript/React/backend fundamentals with practical RAG and agent-building experience, including a poker coaching assistant built solo from ingestion and retrieval through prompt tuning and evaluation.”

“Frontend-focused engineer building production-grade, browser-based AI products, including a live clinical decision support platform for Stanford clinicians. Stands out for combining sophisticated React/TypeScript streaming UI architecture with strong browser performance expertise, including Core Web Vitals and rendering-pipeline optimization.”

Kinjal Patel

Screened

Senior Software Engineer specializing in distributed systems and FinTech

Austin, TX7y exp

“Backend/full-stack engineer with hands-on experience building a core consent management service for an open banking platform using Spring Boot microservices, Kafka, Redis, and secure REST APIs. Stands out for combining domain knowledge in financial data sharing and consent controls with practical system design that improved customer self-service and reduced support dependency.”

“Full-stack engineer with hands-on ownership of a production telemedicine dashboard at Cleveland Clinic, spanning React/TypeScript frontend, Spring Boot and Node.js backend services, and AWS/Kubernetes infrastructure. Stands out for real-time systems expertise, measurable performance improvements in a healthcare setting, and a clear interest in applying that backend/cloud foundation to AI agent systems.”

Sri Teja Adimulam

Screened

Mid-level AI Engineer specializing in LLM systems and cloud data platforms

Detroit, MI5y exp

“Customer-facing technical professional with 5+ years supporting AI, analytics, and automation solutions in enterprise environments. Currently helping build and deploy a multi-agent AI system for roadside assistance operations, with strong experience translating complex business workflows into demos, pilots, integrations, and secure production-ready architectures.”

“Full-stack engineer who shipped a production "Financial Insight" assistant dashboard in Next.js App Router/TypeScript, integrating a RAG pipeline (embeddings + ChromaDB + LLM) via route handlers and owning post-launch performance (latency, token cost, retrieval relevance). Also built/optimized Postgres-backed workflows for an outbound dialer and callback routing engine handling ~10,000 daily contacts, validating query performance with EXPLAIN (ANALYZE, BUFFERS).”

“Startup-style fund operations professional from Servicy, an outsourced finance and operations partner to venture capital funds, who grew from business analyst into owning full fund workflows across reporting, cash management, investor communications, audit prep, and planning. Particularly interesting for roles needing analytical rigor plus operational structure: she built audit frameworks, automated manual processes, advised GPs on fund-structure incentives, and developed reporting infrastructure for a crypto fund.”

“Backend/data platform engineer who owned end-to-end production services for a fleet analytics/GenAI platform, spanning FastAPI microservices on Kubernetes and AWS (EKS + Lambda) event-driven workloads. Strong in reliability/observability (OpenTelemetry, circuit breakers, idempotency), data pipelines (Glue/Airflow/Snowflake), and measurable performance/cost wins (SQL 10s to <800ms P95; ~30% compute cost reduction).”

“AI/LLM practitioner at iFrog Marketing Solutions who drove a RAG chatbot from prototype to production in a legacy, AI-resistant environment by validating customer needs and building a business case. Implemented production-grade LLM practices (CI/CD eval gating, rollbacks, prompt/context engineering) and led internal workshops to bring non-AI-native developers up to speed while partnering with sales on tailored demos to drive adoption.”

“GenAI engineer who has deployed production LLM/RAG chatbots for internal document search, focusing on reliability (hallucination reduction via prompt guardrails + retrieval filtering) and performance (latency improvements via caching). Experienced with LangChain/LangGraph orchestration for multi-step agent workflows and iterates using monitoring/logs and benchmark-driven evaluation while partnering closely with product and business teams.”

“Backend/web API engineer with hands-on experience deploying .NET Core APIs to Azure App Service and stabilizing production systems through disciplined log-driven troubleshooting, environment configuration management, and SQL performance tuning (execution plans, query rewrites, indexing). Has also debugged cross-layer incidents involving DB locks and network latency, and modifies Python/XML automation scripts to meet customer-specific requirements while improving logging and performance.”

“GenAI/ML engineer with production experience at Cognizant and Ally Financial, building end-to-end LLM/RAG systems and ML pipelines. Delivered a domain chatbot trained from 90k tickets and 45k docs, improving intent accuracy (65%→83%), scaling to 800+ concurrent users with 99.2% uptime and sub-150ms latency, and driving +14% customer satisfaction. Strong in Azure ML + DevOps CI/CD, Dockerized deployments, and explainable/PII-safe modeling using SHAP/LIME to satisfy stakeholder trust and GDPR needs.”

“Built and deployed a production real-time voice AI support agent that answers inbound calls, identifies callers, troubleshoots via a knowledge base, and automatically creates/updates tickets with escalation to humans when needed. Demonstrates strong reliability/latency engineering (streaming, schema validation, idempotency, DB constraints) and uses LangGraph state machines plus OpenAI Agents SDK for multi-agent routing, with KPI-driven testing and monitoring.”

“Built a college AI chatbot during a master’s program, owning the full Python/Flask backend plus Google Gemini integration and a Postgres persistence layer (course info + conversation history), including caching/performance tuning. Also deployed and migrated ETL/ELT workloads from AWS Lambda into Kubernetes/EKS with GitHub Actions-based GitOps CI/CD, IRSA permissions, and Secrets Manager/S3/Postgres connectivity.”

“JavaScript/React performance-focused engineer who contributed upstream to an open-source virtualization/pagination library, fixing overlapping-fetch race conditions and introducing prefetch/deduping patterns that cut load times from ~3s to <900ms and reduced render thrash ~35%. Also built healthcare automation systems (clinical summary and claims triage), including a FastAPI + RAG pipeline that retrieved CPT/ICD evidence, improving decision accuracy from 67% to 86% and reducing turnaround time by 40%.”

“Built and productionized an agentic LLM workflow during a summer internship to transform unstructured clinical reports into analytics-ready structured data, using a LangChain multi-agent design plus an LLM-as-a-judge layer to control quality in a regulated setting. Also has experience orchestrating ML pipelines at Piramal Capital using AWS Step Functions/EventBridge/CloudWatch, with strong emphasis on observability, evaluation rigor, and measurable impact (80–90% reduction in manual data entry).”

“Product-focused full-stack engineer who built and shipped an LLM-powered document-to-flashcard conversion pipeline end-to-end (backend + React/TypeScript UI) in ~10 days. Experienced with event-driven queue/worker systems (Redis/BullMQ), PostgreSQL performance tuning, and AWS production operations, including resolving real scaling incidents and driving reliability from ~70% to nearly 100%.”

“Engineer with Deloitte experience building real-time analytics products and scalable Kafka/Go/Postgres pipelines, plus production LLM features using RAG and embeddings. Demonstrates strong focus on performance, reliability, and guardrails/evaluation loops to reduce hallucinations and improve real-world AI system quality.”

“Built an end-to-end automated trading system for Polymarket, including Go/Python execution services, Terraform-scheduled ETL/feature pipelines, and monitoring on modest hardware. Also shipped a production LLM+RAG signal verifier/explainer that grounds trade decisions in external context (news/social) with vector DB retrieval and guardrails, plus a lightweight RAGAS-style eval loop on ~50 resolved markets that improved signal faithfulness by ~15%.”

“Early engineer at a fast-growing startup who owned an AI-powered portfolio/site generation workflow end-to-end (frontend in Next.js App Router/TypeScript through backend orchestration). Emphasizes server-first security/performance (Server Components/Actions, revalidation), and production hardening with validation, caching, observability, retries/idempotency, and CI/E2E testing.”

“Backend/full-stack engineer from Foxconn Industrial Internet who led development of a production TypeScript/Node.js facility monitoring platform delivering near real-time manufacturing metrics (e.g., downtime and OEE) using MySQL + InfluxDB and a React dashboard. Demonstrates strong production operations mindset with queue-based workers, idempotency/DLQ patterns, structured observability, and automated Docker + GitLab CI/CD deployments.”

“Built and launched a personalized sprint-planning dashboard to reduce recurring planning friction, choosing a simple, reliable scoring approach over a complex model. Iterated based on team feedback (more control, dependency clarity, performance), achieving a reported 20% drop in task spillovers; transparent about not yet shipping production LLM/RAG features but actively learning.”

“Built and shipped production RAG-based LLM agents automating multi-step document query workflows, emphasizing reliability via monitoring, retries, structured exception handling, and fallback retrieval (alternative embeddings/keyword search). Demonstrated measurable gains (18% latency improvement, 25% retrieval efficiency, 12% precision) and has experience integrating agents with messy tax and transaction data at RSM using validation/cleaning and idempotent design.”

Yukta Chikate

Screened

Mid-level Machine Learning Engineer specializing in safety-critical and uncertainty-aware ML systems

Brooklyn, NY5y exp

“Built and productionized an LLM-powered assistant for company documents and support questions, focused on reducing time spent searching PDFs/policies/tickets while preventing hallucinations by grounding answers in approved sources. Demonstrates strong production engineering (Kubernetes/orchestration, caching, monitoring, fallbacks) plus security-minded permissioning and close collaboration with operations/support stakeholders.”