.png){kind=logo}
Vetted Sentiment Analysis Professionals
Pre-screened and vetted.
Mid-level Software Engineer specializing in backend systems, cloud, and AI
Junior Business Analyst specializing in growth analytics and experimentation
Mid-level Full-Stack Software Engineer specializing in cloud-native web and real-time systems
Mid-level AI/ML Engineer specializing in production ML, NLP, and computer vision
Senior Digital Analyst specializing in marketing analytics, personalization, and MarTech
Mid-level Machine Learning Engineer specializing in GenAI, LLM agents, and MLOps
Mid-level AI Engineer specializing in LLMs, RAG chatbots, and cloud AI testing
Mid-level AI/ML Engineer specializing in NLP, Computer Vision, and Generative AI
Mid-level Full-Stack Software Engineer specializing in cloud-native microservices
Intern AI/ML Engineer specializing in generative AI and multimodal agentic systems
Executive VP of Engineering specializing in FinTech platforms, cloud modernization, and AI/ML
Executive Technology & Data Leader specializing in AI/ML strategy and digital transformation
Senior AI/ML Engineer specializing in LLM systems and conversational AI
Mid-level Full-Stack Software Engineer specializing in cloud-native and AI-driven applications
Staff Full-Stack Software Engineer specializing in cloud-native microservices
Senior Data Scientist specializing in GenAI, LLMs, and Analytics Engineering
Whitney Stout
Director-level Technology Architect specializing in GTM systems, data, and AI
“Bootstrapped founder of ByteThirst, a launched browser extension and CLI that monitors and estimates the environmental cost of LLM usage. They are targeting a niche climate-tech/AI space with support for 14 platforms, a freemium subscription model, and a first-mover thesis backed by market research, market sizing, and profit projections.”
Sahithi S
Mid-level AI/ML Engineer specializing in NLP, MLOps, and Generative AI
“Built and deployed a production generative AI chatbot at NVIDIA using LangChain + GPT-3 integrated with internal data sources, cutting response time nearly in half and improving CSAT by ~12 points. Also delivered LLM-driven QA tools by fine-tuning Hugging Face transformer models and deploying via an AWS-based pipeline (Lambda/Glue/S3) with orchestration (Airflow/Step Functions), CI/CD, Kubernetes, and monitoring (MLflow/Splunk/Power BI).”