.png){kind=logo}
Vetted Machine Learning Professionals
Pre-screened and vetted.
Director-level Engineering Leader specializing in personalization platforms, MLOps, and GenAI
Mid-level AI/ML Engineer specializing in LLMs, RAG, and scalable MLOps
Mid-level Full-Stack Software Engineer specializing in FinTech analytics and security
Senior Software Engineer specializing in cloud architecture and machine learning
Director-level Product & Business Leader specializing in AI-driven consumer and global growth
Junior AI/ML Software Engineer specializing in NLP, LLM evaluation, and recommendation systems
Senior Software Engineer specializing in Python, cloud infrastructure, and AI-powered search
Mid-level Software Engineer specializing in backend APIs, data pipelines, and cloud microservices
Senior AI/ML & Data Scientist specializing in NLP, knowledge graphs, and semantic search
Executive Technology & Resilience Leader specializing in AI, multi-cloud, SRE, and cyber resilience
Executive FinTech Founder and Software/Finance Leader specializing in data pipelines and valuation
Principal business value leader specializing in AI, data, and cloud transformation
Executive CTO and serial founder specializing in AI platforms and Financial Services
Mid-level Machine Learning Engineer specializing in Generative AI and LLM applications
Principal Software Engineering Manager specializing in cloud platforms and security
Jared Alessandroni
Executive CTO specializing in AI, cloud platforms, and scaling SaaS products
“NYC-based startup founder/CTO who sold products to Omnicom and Sprinklr, then built an AI-powered cultural insights engine inside Omnicom using AWS Lambda + ML to process ~1M items/day and reached ~$1MM ARR in year one. Former senior leader at Sprinklr managing 200+ people globally, delivering enterprise martech solutions with SLAs and high-reliability social data pipelines (Twitter firehose).”
David Neiman
Mid-level Robotics Researcher specializing in motion planning and vehicle routing
“CMU robotics PhD/PhD researcher and former CMU Robotics Club project lead who built a novel Bayes-filter-based system to localize within music so robotic instruments can follow a human’s tempo in real time. Also works on simulation-heavy multi-agent vehicle routing with traffic-signal scheduling, optimizing for real-time performance via profiling, multithreading, and neural-network surrogates for signal control.”
Tanisha Gupta
Intern Computer Vision/Perception Engineer specializing in synthetic data and 3D/4D world modeling
“Embodied AI/robotics-focused ML engineer who built a real-time assistive Braille device by coupling transformer OCR with an Arduino-controlled electromechanical Braille cell, solving tight latency and hardware-integration constraints. Has recent work on geometry-grounded world models and a real-time 4D reconstruction foundation model (Any4D), and delivered measurable impact at Voxel AI by building a scalable headless simulation + synthetic data pipeline that improved a safety-critical algorithm’s recall by ~16%.”
Kanna Rajagopal
Executive Engineering Leader specializing in cloud services, distributed systems, and networking
“Amazon engineering leader (15+ years) targeting Senior Manager/Director roles, with deep ownership of contact-center latency and reliability initiatives. Shipped a global production improvement cutting call latency 30–40% and led a complex Citrix SDK integration, including incident response and a backward-compatible rollout strategy to protect existing customers while enabling new features.”
Prasanth Peddireddy
Entry-level Supply Chain & Test Engineer specializing in warehouse automation and robotics
“P&G operator who is also building and selling an AI receptionist (voice agent) SaaS for healthcare/service clinics, using EHR + calendar API compatibility to target accounts and letting the Voice AI run parts of the demo to prove value. Has already closed and deployed to two clients in the last two months, with production impact via reduced front-desk overhead and automated scheduling/FAQs, and brings a structured, scalable deployment/process mindset from global WMS rollouts.”