Vetted Model Evaluation Professionals

“Robotics software engineer with hands-on experience building a vision-guided grasping pipeline on a 7-DOF Franka arm, implementing gradient-based IK with null-space optimization and RRT* motion planning in ROS1. Strong in sim-to-real deployment and real-world debugging—addressed frame misalignment via hand-eye calibration and centralized TF configuration, and reduced replanning/jitter by tuning a weighted pose filter using rosbag replay and variance/grasp-time metrics. Also built an ESP32-based mobile robot architecture combining embedded decision-tree control with WiFi/web high-level commands.”

“ML/LLM engineer who has built and productionized a large multimodal LLM pipeline end-to-end—fine-tuning a 20B+ parameter model with distributed/FSDP training and deploying on Kubernetes via Triton for ~5x throughput. Strong focus on reliability and safety (monitoring with SHAP, guardrails, A/B testing) with reported ~22% relevance lift and reduced harmful/incorrect outputs, plus experience orchestrating ETL/retraining workflows with Airflow across S3/Snowflake/RDS.”

Gaurav Narasimhan

Screened

Executive AI platform leader specializing in autonomous agent systems for enterprise SaaS

Redwood City, CA26y exp

“Real estate entrepreneur who has previously raised capital from friends and family and has engaged directly with VC firms including A16z. Demonstrates unusually strong founder commitment, having worked two full-time jobs for the past six years while pursuing entrepreneurial goals, with hypothesys.ai described as the outcome.”

“AI/ML engineer with experience spanning Accenture healthcare NLP systems, academic research, and Apple on-device LLM integration. Stands out for owning regulated production pipelines end-to-end—from HIPAA-compliant clinical NLP and EHR integrations to incident prevention, experiment tracking, and optimized on-device inference with LLaMA 3.”

“Unity/C# gameplay engineer with strong systems architecture depth who has reworked core gameplay ability frameworks, shipped across mobile and standalone VR, and solved multiplayer synchronization issues with server-authoritative netcode. Also brings an unusual crossover into AI tooling, having owned an AI-powered debugging assistant at Arm and integrated LLM workflows into CI/development pipelines.”

“Senior data/AI and platform leader with Walmart- and T-Mobile-scale architecture experience, including building real-time inventory + forecasting platforms (Kafka/Cassandra/Hadoop) and Azure IoT systems. Known for translating board-level business goals into roadmaps that deliver measurable impact (e.g., $50M savings and $250M profit in a year; +2% conversion via Customer 360) and for hands-on problem solving in ML/forecasting (feature reduction and LASSO).”

Patrick Jose

Screened

Senior AI Engineer specializing in LLM applications and full-stack systems

San Francisco, CA8y exp

“Built and owned a production LLM/RAG customer support assistant end-to-end, from prototype through deployment, monitoring, and iteration. Their work automated roughly 40% of common support queries and cut response times by about 30%, while also creating reusable Python inference services that improved consistency and team velocity.”

“Atlassian ML/AI engineer who has shipped end-to-end production systems combining classical ML, streaming infrastructure, and LLM-based personalization to improve onboarding and free-to-paid conversion. Particularly strong in turning research-style RAG and reranking ideas into low-latency, reliable product systems with robust evaluation, safety guardrails, and reusable platform services for other teams.”

YAKKALI PAVAN

Screened

Mid-level Machine Learning & Generative AI Engineer specializing in NLP, CV, and RAG systems

USA6y exp

“Built and deployed a production LLM-powered RAG document intelligence system used by non-technical enterprise stakeholders, cutting document search time by 40%+ while improving answer consistency. Demonstrates strong MLOps/data workflow orchestration (Airflow, AWS Step Functions, managed schedulers across GCP/Azure) and a metrics-driven approach to reliability, evaluation, and cost/latency optimization with guardrails and observability.”

Durgaprasad G

Screened

Mid-level AI/ML Engineer specializing in LLM infrastructure, RAG, and agentic systems

New York City, NY3y exp

“Stripe engineer who owned and unified multiple team RAG systems into a shared production platform used by 200+ internal operators, deployed on EKS with Kafka ingestion and hybrid retrieval. Drove measurable business outcomes including <400ms latency, ~35% inference cost reduction, ~25% accuracy lift via fine-tuning, and real-time auto-approval of 80%+ merchant compliance applications through strong observability and reliability patterns.”