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Vetted Sentiment Analysis Professionals
Pre-screened and vetted.
Principal Data Scientist specializing in AI and quantitative investment strategies
Mid-Level Software Engineer specializing in Ads Serving and Machine Learning Systems
Mid-level AI/ML Engineer specializing in LLMs, RAG, and multi-agent systems
Mid-level AI/ML Engineer specializing in NLP, computer vision, and recommender systems
Senior Machine Learning Scientist specializing in LLMs, RAG, and health AI
Kusumita Dasgupta
Director-level Engineering Leader specializing in data platforms, cloud systems, and LLM products
“Engineering leader/player-coach with recent hands-on work delivering an agentic AI MVP on Amazon Bedrock (conversational UI + supervisor agent routing between internal knowledge and external sources). Previously drove large-scale data platform cost optimization at Twitter, saving ~$3M–$5M annually, and has owned production incidents end-to-end with a focus on analytics/monitoring improvements and team coaching.”
Harish Kasu
Mid-level AI/ML Engineer specializing in Generative AI, RAG, and MLOps
“AI/LLM engineer with production experience at NVIDIA and Microsoft, including building a RAG-based enterprise knowledge assistant that improved accuracy by 42% and scaled to thousands of queries. Deep in inference optimization (TensorRT-LLM, Triton, quantization, speculative decoding) and MLOps/observability (Prometheus/Grafana, MLflow, LangSmith), plus orchestration with Kubeflow/Airflow across multi-cloud.”
Mid-level Machine Learning Engineer specializing in LLMs, RAG, and scalable GPU inference
Staff Data Scientist / AI-ML Engineer specializing in fraud detection, NLP, and recommendations
Executive Technology Leader specializing in FinTech and large-scale data platforms
Mid-level Machine Learning Engineer specializing in generative AI, NLP, and MLOps
Mid-level AI/ML Engineer specializing in LLM training, RAG, and low-latency inference
Sumer Joshi
Senior Full-Stack Engineer specializing in backend systems and AI applications
“Candidate is deeply focused on AI-native software development, using a deliberate planner/implementer agent workflow with tools like Cursor, Claude, and Kimi. They also built a personal project called Config Proctor, an AI-agent-driven Terraform/AWS self-healing system that identifies infrastructure configuration gaps and proposes fixes.”
Sai supriya
Mid-level AI/ML Engineer specializing in LLM alignment, safety, and scalable inference
“Built and productionized an AWS-hosted, Kubernetes-orchestrated RAG assistant that enables natural-language Q&A over internal document repositories with grounded answers and citations. Demonstrates strong applied LLM engineering: hallucination mitigation, hybrid retrieval + re-ranking, and rigorous evaluation via benchmarks and A/B testing, plus real-world scaling of compute-heavy inference with dynamic batching and monitoring.”
Wendy Liu
Mid-level growth marketing manager specializing in digital strategy and analytics
“Early-career candidate with internship experience spanning podcast/community outreach and B2B SaaS outbound campaigns. They stand out for combining personalized research, webinar-led demand generation, and data-driven email optimization, citing results such as 60% open rates, roughly 100 booked consultations, and a 40% conversion lift from CTA changes.”
Gabrielle Burns
Senior AI/ML Engineer specializing in computer vision, NLP, and enterprise ML systems
“ML/AI engineer with hands-on ownership of production computer vision and GenAI systems, spanning real-time public safety video analytics and RAG-based knowledge assistants. Stands out for translating research-oriented approaches into scalable, monitored production systems with clear business impact, including 50% latency reductions, 25% faster response times, and 40% lower document search time.”
MARCUS JOHNSON
Senior AI/ML Engineer specializing in Generative AI, NLP, and RAG systems
“ML/NLP engineer focused on production-grade data and search/recommendation systems: built an end-to-end pipeline that connects unstructured customer feedback with product data using TF-IDF/BERT, Spark, and AWS (SageMaker/S3), orchestrated with Airflow and monitored for drift. Also has hands-on experience with entity resolution at scale and improving search relevance via BERT embeddings, FAISS vector search, and domain fine-tuning validated with precision@k and A/B testing.”
Mid-level AI/ML Engineer specializing in NLP, computer vision, and MLOps