Vetted Model Evaluation Professionals

“Data/ML practitioner with experience spanning medical imaging (retinal vessel analysis for hypertension/CVD risk prediction) and enterprise data engineering at Carl Zeiss. Built large-scale SAP data cleaning/validation pipelines (10M+ daily records, ~99% accuracy) and RAG-based semantic search with LangChain/vector DBs that cut manual querying by 82%, plus automation that reduced data onboarding from 8 hours to 12 minutes.”

“Candidate brings an operator-to-founder profile with leadership experience in IT and Business Systems and a strong grasp of how ideas become venture-backable products. They speak fluently about startup evaluation criteria such as TAM, technical defensibility, speed to scale, and AI differentiation, and appear especially motivated by building solutions end-to-end in startup or venture studio environments.”

“Candidate comes from SaaS and healthcare analytics rather than game development, but has strong end-to-end ownership experience building real-time, high-availability systems in Python/AWS. They highlight measurable impact across performance, throughput, uptime, and cost reduction, including queue optimization and predictive ICU utilization pipelines, and are looking to transfer that systems engineering foundation into Unity/gameplay work.”

“Built and deployed a production LLM-powered multi-agent compliance copilot (life sciences/finance) using LangChain/LangGraph + RAG over vector databases, delivered via async FastAPI on Kubernetes. Emphasizes audit-ready, deterministic outputs with schema constraints and citations, plus rigorous evaluation/monitoring; reports 60%+ reduction in manual research time and successful production adoption.”

“Built a production RAG-based GenAI copilot backend at Aetna using Python/FastAPI, GPT-4, LangChain, and Azure AI Search, deployed on AKS with Prometheus/Grafana observability. Owned the system end-to-end (ingestion through deployment) and improved peak-time reliability by addressing vector search and embedding bottlenecks with Redis caching, index optimization, and async processing, plus added anti-hallucination guardrails via retrieval confidence thresholds.”

“Full-stack engineer who built and owned a production real-time computer-vision inference platform at Credence, spanning Next.js App Router/TypeScript frontend with SSE/WebSocket streaming, a Flask backend, and Postgres analytics. Demonstrated measurable performance wins (70% fewer re-renders; latency cut to ~40–50ms) and strong production rigor (durable orchestration, idempotency, observability, AWS EC2 + CI/CD) with tight post-launch UX iteration based on analyst feedback.”

“AI engineer who built a production RAG-based internal analyst tool at BlackRock, fine-tuning an LLM on proprietary financial data and adding four layers of guardrails (input/retrieval/generation/output) to improve grounding and reduce hallucinations. Implemented a LangChain-based multi-agent orchestration (7 major agents) deployed on AWS ECS, with reliability measured via internal human evaluation, LLM-as-judge, and RLHF/drift monitoring.”

Dylan Tang

Screened

Intern Software Developer and ML Researcher specializing in medical imaging and computer vision

Chicago, IL0y exp

“AI/ML practitioner with experience spanning audio/LLM applications (built "Iota" using Whisper, tiktoken, and a local Ollama-served LLM) and healthcare ML (Facemed.ai; UChicago Radiology). Demonstrates a production-oriented mindset—focus on data/model fit, deterministic field testing, and operational safeguards—and has improved research evaluation workflows via a hash-table-based concurrent model tracking approach.”

Prateeksha Ranjan

Screened

Mid-level Software Engineer specializing in embedded AI and full-stack systems

Irvine, California4y exp

“Robotics software engineer who built and owned core navigation components for a TurtleBot in ROS/ROS2 and Gazebo, including an RRT-based planner, waypoint-to-velocity motion planning, and PID trajectory tracking. Demonstrates strong real-time debugging skills (control-loop timing under CPU load), costmap/occupancy-grid tuning, and distributed ROS2 communication design using DDS/QoS, plus Docker and CI/CD automation experience from Keysight.”

Chia-En Lu

Screened

Junior AI/ML Systems Engineer specializing in LLM infrastructure and distributed training

1y exp

“Built and shipped a production NMT system translating medical documentation for a rare/low-resource language, tackling data scarcity with retrieval-driven pattern matching plus dictionary/grammar- and LLM-based augmentation and validating quality with a linguistic expert. Also develops agentic LLM workflows with LangChain/LangGraph (including a deep-research style system) and has experience aligning medical AI deployments with clinician-defined risk metrics and human-in-the-loop decision making.”

“ML/LLM infrastructure engineer who built and shipped a production internal evaluation + failure-analysis agent (Arthur AI / R3AI context) that orchestrated end-to-end benchmarks with deterministic lineage, regression detection, and root-cause reporting at 5,000+ benchmarks/week. Also built backend observability and data validation systems for analytics pipelines at FullStory processing ~3.4B weekly events, emphasizing schema validation, quarantine fallbacks, and idempotent operations.”