Vetted Large Language Models (LLMs) Professionals

David Alvarado

Screened

Junior Business Analyst specializing in data analytics and BI

Orlando, FL3y exp

“Analytics candidate with insurance domain experience at Chubb, combining strong SQL/Python data engineering for claims reporting with business-facing metric design in Power BI. Also built an MLB game outcome predictor that beat Vegas implied probabilities using public data, showing strong product thinking and applied modeling ability beyond standard BI work.”

“Early-career full-stack builder who created an AI interview-prep platform used by 200+ students, tested it with a 25-student study group, and earned recognition through the CUNY Startup accelerator, including prize money and local college adoption. Has also shipped compliance-sensitive AI products in healthcare marketing and operational tools like invoice approval systems, showing unusual breadth across AI, UX, and backend systems.”

“Built OpenGPU features spanning React/TypeScript, Go orchestration, PostgreSQL, Redis, and Stripe, with a strong focus on reliability, transaction integrity, and low-latency distributed systems. Also shipped LLM product infrastructure, including persona-conditioned frameworks and reusable prompt/model abstractions, showing a blend of systems engineering and fast product iteration.”

“Built and deployed an end-to-end fintech product, FinSight, for bank statement analysis and financial Q&A using a production-style RAG architecture. Stands out for combining FastAPI, OpenAI embeddings, FAISS, hybrid SQL/vector retrieval, and practical reliability work like chunking optimization, validation, and low-latency performance tuning.”

Naveen K

Screened

Mid-level Full-Stack Software Engineer specializing in AI-powered backend systems

San Francisco, CA2y exp

“Full-stack engineer with hands-on ownership of a real-time analytics and alerting dashboard built with React/TypeScript, Node.js, Kafka, Redis, and PostgreSQL. Also contributed to an internal LLM-powered support automation system, focusing on backend orchestration, RAG-based reliability, and Kubernetes deployment. Stands out for combining product-minded zero-to-one execution with strong distributed systems and AI integration experience.”

“Built end-to-end AI products across fintech and healthcare, including a real-time loan risk prediction system and a patient feedback insights platform. Stands out for combining full-stack delivery, production ML/MLOps on AWS, and pragmatic human-in-the-loop safeguards; reported a 22% improvement in prediction accuracy.”

“Backend/cloud engineer who built an AWS serverless IoT system that computes Bluetooth beacon locations from telemetry using heavy scientific Python (NumPy/SciPy/pandas) packaged as Dockerized Lambda, integrated with Java microservices and scheduled batch orchestration. Has deep AWS delivery experience (CI/CD with Code* tools, CloudFormation, cost controls) and has led high-severity incident response including CloudTrail forensics and infrastructure recovery after a compromised-keys crypto-mining attack.”

“Product leader and founder/co-founder type who helped build AI products from concept through pilot, including Mira, an AI-powered tourism platform that reached 60K users and 12K engagements during a Tuscany pilot. He appears especially strong at translating between executives and technical/design teams, structuring cross-functional delivery, and applying human-centered AI principles in education through a RAG-based K-12 tutoring product, TUUTOR.”

Sravya Chunduri

Screened

Mid-level AI/ML Engineer specializing in LLM, NLP, and MLOps

Virginia, USA4y exp

“AI/ML Engineer with 3+ years of experience spanning RAG pipelines, MLOps, large-scale data workflow automation, and resilient Playwright-based UI automation. At Black Hawk Network and Wipro, they describe shipping production systems with strong observability and compliance controls, including reducing flaky automation failures from 30% to under 2% and automating 3+ TB/day reconciliation workflows.”

“Full-stack product engineer with hands-on experience shipping React/TypeScript applications on AWS serverless infrastructure with Postgres. Stands out for combining measurable performance optimization (~30% faster APIs), UX improvements that lifted activation by 25%, and pragmatic platform thinking through reusable hooks and safe multi-tenant dashboard customization.”

Varun Sharma

Screened

Mid-level AI Builder and Data Engineer specializing in GenAI and data pipelines

Remote, USA4y exp

“Full-stack AI product engineer who personally built ViGenAir, a multimodal system that turns long-form video into ads using FastAPI, React, and agentic scoring. Stands out for handling complex 50GB+ media pipelines, re-architecting systems to eliminate OOM failures, and making opaque AI workflows usable through interactive visual UX that improved trust, speed, and retention.”

“Early-stage founder with a privacy-focused concept to simplify at-home self-hosting for services like email, photos, files, and local AI. They are approaching the idea in staged phases to test traction and minimize upfront capital, and have some familiarity with the startup accelerator ecosystem through a Techstars-affiliated company.”

Surya Padmini Konjula

Screened

Mid-level Software Engineer specializing in backend, cloud, and AI for FinTech

San Jose, CA6y exp

“Senior full-stack engineer focused on AI-powered workflow automation and customer support products, with hands-on ownership from React/TypeScript UI through FastAPI microservices, retrieval pipelines, and Kubernetes deployment on GCP. Particularly strong in turning ambiguous zero-to-one AI initiatives into production systems that reduce manual operations, improve turnaround time, and remain reliable through strong orchestration and monitoring practices.”

“Backend and distributed-systems engineer with experience integrating LLM capabilities into clinical data workflows at CVS. Stands out for treating AI as an engineering accelerator rather than a shortcut, with strong emphasis on validation, observability, Kafka-based async pipelines, and safe multi-agent orchestration for production systems.”

“Senior product leader who helped transform Submittable from a single-program grant tool into a multi-program impact platform, driving ARR from $20M to $70M+ while improving retention and margins. Particularly strong in enterprise platform strategy and human-centered AI, with a clear philosophy of using AI to augment expert judgment rather than replace it.”

“Built and shipped a production LLM-powered agent for supply chain operations that integrates ERP data and automates multi-step decision-making with tool calling, state management, and structured JSON outputs. Emphasizes production reliability (guardrails, fallbacks, monitoring, idempotency) and reports strong business impact: 40% faster decisions, 30% higher throughput, and 25% efficiency gains.”

“Built and shipped a production LLM-driven data harmonization/record-matching pipeline for pharmaceutical datasets, combining normalization, embeddings/vector search, and an LLM validation step. Emphasizes production reliability via guardrails, confidence thresholds, idempotent/retryable stages, and human-in-the-loop fallbacks, with monitoring focused on manual review and error rates to reduce false positives.”

“Post-graduate, self-directed robotics/RL practitioner who independently built a modular reinforcement learning training framework in Python using Stable-Baselines3, Gymnasium, and PyTorch. Emphasizes reproducible experimentation (multi-seed validation), simulation (PyBullet/Box2D), and systematic comparison of algorithms/environments via a factory-pattern architecture.”

“Built Hexagon’s production Text-to-CAD Copilot that converts text and rough sketches into editable CAD code, combining GraphRAG (Neo4j/LangChain) with a Gemini-powered vision module and multi-agent geometric validation—cutting manual modeling from a day to ~45 seconds and driving retrieval latency below 50ms. Also has large-scale GCP data/ML orchestration experience (Airflow/Cloud Composer, Dataflow, Pub/Sub, Snowflake) processing 50M+ daily records with drift monitoring and automated reliability controls.”

“Software engineer with healthcare domain experience (patient monitoring and provider systems) who improves reliability and performance in complex React/Flask applications. Led API standardization for shared internal React utilities using an RFC + deprecation strategy, and optimized a live WebSocket dashboard to handle 3000+ concurrent clinics while reducing client CPU usage. Strong in production debugging, data ingestion validation, and operational improvements like structured logging and alerting.”

“Backend engineer with airline operations domain experience who modernized flight-ops systems from batch updates to real-time streaming on AWS (Kafka + Spring Boot microservices), improving latency and stability through metric-driven tuning and idempotency. Also shipped a production LLM decision-support component using RAG over operational logs and internal procedures, with strong guardrails and an evaluation/regression loop to reduce hallucinations and enforce grounding.”

Eric Guzman

Screened

Senior Solutions Architect specializing in MLOps and AI platform operations

New York, NY7y exp

“Audio/music editor and mixer with Symphony Space promotional work (e.g., Uptown Showdown, Selected Shorts), focused on shaping emotion and pacing through tempo automation, tension-building harmonic choices, and precise cut-to-music timing. Pro Tools certified (Institute of Audio Research) with hands-on mixing workflows across Logic, Reason, and Cubase, and experience iterating based on commercial/producer feedback.”

Ambuk Rehani

Screened

Mid-level AI/Backend Engineer specializing in RAG and data platforms

Dallas, TX7y exp

“Built and shipped a production LLM-powered financial Q&A interface that extracts precise numeric data from PDFs using a hybrid AWS Textract + LLM normalization pipeline, with confidence gating and guardrails to prevent unreliable answers. Experienced with LangChain-based RAG orchestration (chunking, memory, structured outputs) and collaborated closely with PMs/analysts on IRS Form 990 extraction requirements.”

Ido Avnon

Screened

Junior Software Engineer specializing in AI, geospatial, and full-stack applications

Seattle, WA5y exp

“Early-stage startup engineer with experience at pre-seed and roughly Series A-stage companies, including Rage Events Inc. Shipped production full-stack features with Next.js/TypeScript, designed Postgres-backed workflow systems, and took ownership of post-launch support and fixes in fast-moving environments.”