Vetted OpenAI API Professionals

“Software engineer with production experience building a serverless monolith and multi-layer video pipeline at easyML, plus hands-on integration of multiple LLM providers (Grok/Claude/OpenAI) into a full-stack app. Interested in robotics via computer vision (OpenCV/OpenMMLab), with a strong real-time systems mindset around SLOs, latency, determinism, and reliability; also has low-level OS experience writing a keyboard device driver.”

“ML/NLP practitioner focused on knowledge-graph-based retrieval for LLM question answering, including an urban/autonomous-vehicle decision-making use case. Built a hierarchical GraphRAG + vector database system and an entity-resolution pipeline that blends spatial and semantic similarity, validated using LLM-generated synthetic datasets; uses Python tooling like RDFLib, GraphDB, OpenAI APIs, and LangChain.”

“ML/NLP engineer who built a production system that converts large-scale unstructured text into a connected, searchable knowledge base using spaCy + Sentence Transformers/FAISS and a Neo4j knowledge graph, with BERTopic and XGBoost for organization/labeling. Strong focus on production-grade Python workflows (FastAPI/Celery, Pydantic validation, Docker, AWS ECS/Lambda) and robust entity resolution with measurable precision/recall and human review for low-confidence matches.”

“AI engineer with hands-on production experience building an end-to-end RAG system that reduced document-answering time from hours to minutes, improving accuracy through chunk overlap and hybrid BM25+semantic retrieval. Also built a LangGraph-based agent that researches company financial news via web search (Google Serper), using Pydantic structured outputs and checkpointing for reliability; experienced collaborating with non-technical stakeholders at JPMC and communicating ROI.”

“Built and shipped a production LLM-powered internal operations automation platform using LangChain RAG (Pinecone) and FastAPI microservices, deployed on AWS EKS, serving 10k+ daily interactions. Implemented a rigorous evaluation/observability stack (golden datasets, prompt regression tests, MLflow, retrieval metrics, hallucination monitoring) that drove hallucinations below 2% and improved reliability, and partnered closely with non-technical ops leaders to cut manual lookup work by 60%+.”

“Built a production internal LLM-powered knowledge assistant using a RAG architecture (Python, LLM APIs, cloud services) that answers employee questions with sourced, grounded responses from internal documents. Demonstrates strong practical depth in retrieval tuning (chunking/metadata filters), orchestration with LangChain, and production reliability practices (latency optimization, automated embedding refresh, evaluation metrics, logging/monitoring) while partnering closely with non-technical operations teams.”

“Runs two lean real estate companies remotely by building local on-the-ground contact networks and leveraging free-tier technology to keep total annual business costs under $100. Brings a cost-elimination and MVP/validation-first mindset, preferring to join an established company unless a clearly viable business idea emerges.”

“Backend/data engineer with hands-on production experience across GCP and AWS: built FastAPI microservices on Cloud Run and delivered AWS Lambda + ECS Fargate systems with Terraform/GitHub Actions. Strong in data engineering (Glue/Spark, S3/Redshift) and modernization (SAS to Python/SQL), with proven reliability and incident ownership—including cutting a 20+ minute reporting query to under 2 minutes.”

“Built a personal product, Pilly AI—an AI-powered e-commerce product Q&A widget embedded via a simple script tag and served via Cloudflare CDN—covering landing page, backend, database, and deployment end-to-end. Implemented OpenAI integration with prompt/context engineering, JWT-authenticated APIs, and Postgres (NeonDB), and successfully sold the product to a client while shipping in roughly two weeks.”

“AI/LLM engineer who built a production content-generation system for nursing education, combining multimodal RAG over proprietary PDFs (including images) with structured Cosmos DB data and external sources. Strong focus on production reliability—prompt-chaining with LangChain, validation/guardrails, and Azure-based monitoring/observability—plus experience designing Azure AI agents with tool integrations like Bing Search.”

“LLM/ML platform engineer with hands-on experience taking an LLM document summarization prototype into a production-grade service on AWS EKS, emphasizing low-latency inference, drift monitoring, and safe CI/CD rollouts (canary + rollback). Strong in real-time debugging of agentic/RAG systems (tracing, retrieval/index drift fixes) and in developer enablement through practical workshops (Docker/Kubernetes/FastAPI) plus pre-sales support via demos and benchmarks to close pilots.”

“Founding AI engineer (June 2024) at Talon Labs who built and productionized an LLM-powered chatbot for interacting with proprietary supply-chain documents, deployed at large scale (25–100,000 users). Experienced with RAG/LLM orchestration (LangChain, LlamaIndex, Groq AI) and production ops tooling (Kubernetes, Docker, Kubeflow, Airflow), with a metrics-driven approach to evaluation, observability, and stakeholder alignment.”

“Product-focused full-stack engineer (70/30 app vs infra) with Accenture experience and recent AI workflow work, shipping end-to-end systems from React/TypeScript UIs through FastAPI backends to Postgres. Built an AI-driven data extraction platform with async job APIs, strict schema validation, and strong observability, and has operated AWS ECS-based deployments with real incident mitigation (DB connection exhaustion/latency under traffic spikes).”

“Backend engineer who built an AI voice-driven calendar assistant using Python/Flask with a multi-agent architecture (CrewAI + OpenAI), including agent-specific memory management and summarization to handle token limits. Also has production performance optimization experience at Trendyol, adding Redis caching to speed up an advertisement retrieval endpoint and improve page load experience.”

“Forward Deployed Engineer focused on taking enterprise LLM voice agents from prototype to production. Led a turnaround on a high churn-risk account by building a custom nested-API integration and preprocessing layer that enabled the LLM to reason over complex order hierarchies, cutting call handle time from 15 minutes to 2 minutes and driving expansions. Strong in real-time agent/workflow debugging, developer workshops, and sales partnership for adoption.”

“Software engineer at Procter & Gamble focused on warehouse/operations systems, building near-real-time order/inventory visibility using Java/Spring Boot, React, Kafka, PostgreSQL, and Redis with measurable latency and load-time gains. Also shipped internal LLM/RAG knowledge assistants grounded in company runbooks and workflows, implementing guardrails and an evaluation loop that drove concrete retrieval improvements (document chunking) and regression prevention.”

“Backend/DevOps engineer pivoting into robotics/space, building hands-on ROS2 (Humble) skills via Gazebo simulations and experimenting with Nav2 and slam_toolbox. Brings strong distributed-systems and real-time debugging practices (profiling, instrumentation, QoS/retry patterns) and is actively learning perception and control fundamentals to transition into autonomous robotics.”

“Internship experience at Qualitest building and deploying an LLM-powered test automation system that reduced manual test creation and improved efficiency (~40%). Demonstrates strong production engineering for LLM systems (timeouts/retries/monitoring/caching, prompt optimization, batching) and has scaled workflows to 100+ concurrent jobs; also has orchestration experience with AWS Step Functions and Kubernetes.”

“Backend engineer who has shipped production LLM-powered features, including an AI-assisted developer tool on AWS (Spring Boot) and a blog platform capability using embeddings + Elasticsearch for semantic retrieval and LLM-generated summaries/recommendations. Demonstrates practical tradeoff management (quality/latency/cost), guardrails to reduce hallucinations, and evaluation-driven iteration using real user queries and observability via ELK.”

“Customer-facing technical professional with experience supporting LLM/agentic-style workflows and complex integrated systems (APIs, backend logic, databases). Partnered with sales/customer teams at Radix Health to onboard new clients in phased prototypes, translating non-technical requirements into technical scope and implementing core product changes to tailor the appointment-booking solution for providers.”

“Frontend engineer focused on marketing and analytics products, including a real-time multi-touch attribution dashboard. Uses Next.js (SSR/ISR) with React/TypeScript and Tailwind, and emphasizes quality at scale via automated testing, CI/CD (GitHub Actions), feature-flagged staged rollouts, and Mixpanel-driven iteration. Experienced modern state management patterns (React Query + Zustand) and performance tuning (code-splitting, dynamic imports, lazy loading).”

“Open-source JavaScript contributor focused on performance and maintainability in data visualization libraries—refactored legacy ES5 into modular ES6, added tests/docs, and delivered ~30% faster load times with positive community adoption. Also optimized a React dashboard (~40% load-time reduction) and took ownership in an ambiguous AI product initiative by setting milestones, standing up an initial ML pipeline, and shipping a prototype in ~6 weeks that became the basis for production.”

“ML/GenAI engineer with production experience building an LLM-powered governance layer that summarizes verified drift/performance signals into validation reports and release notes, designed for regulated environments with de-identification and non-blocking fallbacks. Strong Airflow-based orchestration background across healthcare and finance, integrating Databricks/Spark and MLflow for scalable retraining/monitoring. Demonstrated ability to partner with non-technical healthcare operations teams to deliver actionable risk-scoring outputs via dashboards and automated reporting.”

“Backend engineer focused on high-throughput Python/Flask systems on AWS, with strong scaling and performance tuning experience (e.g., PostgreSQL join reduced from ~3s to <200ms; background aggregation cut from 10 minutes to <90 seconds with 8x throughput). Has also integrated ML model serving into production APIs (churn prediction) using Celery/Redis batching and AWS Lambda/S3, and designed secure multi-tenant architectures with PostgreSQL schema isolation and row-level security.”