.png){kind=logo}
Vetted Unsupervised Learning Professionals
Pre-screened and vetted.
Mid-level Machine Learning Engineer specializing in LLM inference and MLOps
Mid-level Data Scientist specializing in GenAI, NLP, and deep learning
Mid-level Machine Learning Engineer specializing in LLMs, RAG, and MLOps
Mid-level Machine Learning Engineer specializing in NLP, federated learning, and fraud detection
Mid-level Data Scientist specializing in GenAI, NLP, and deep learning
Mid-level Machine Learning Engineer specializing in recommender systems and LLM/RAG pipelines
Mid-level AI/ML Engineer specializing in GenAI, LLMs, and RAG pipelines
Principal Data Scientist specializing in Generative AI and MLOps
Senior Machine Learning Engineer specializing in GenAI, NLP, and MLOps
Mid-level AI & ML Engineer specializing in NLP, LLMs, and scalable ML systems
Junior Software Engineer specializing in cloud platforms, microservices, and AI/ML
Mid-level AI/ML Engineer specializing in multimodal and generative AI at scale
Mid-level Machine Learning Engineer specializing in MLOps and scalable ML pipelines
Mid-level AI/ML Engineer specializing in NLP, transformers, and RAG systems
Benjamin Kozel
Mid-level Machine Learning & Software Engineer specializing in RAG systems and ML infrastructure
“Built and deployed an in-house RAG LLM system ("MONTY") using LLaMA 3B + FAISS to help teams quickly understand long internal/external specifications. Delivered usable production performance despite severe compute limits (single RTX 3080) by tuning retrieval/reranking and model choice, and is planning a LightRAG/knowledge-graph rewrite to improve accuracy and latency.”
Sairaghav Nissankula
Senior AI/ML Engineer specializing in LLMs, NLP, and enterprise conversational AI
“ML/GenAI engineer with strong end-to-end production ownership across predictive ML, RAG systems, and LLM routing. They pair solid platform engineering skills with measurable business impact, including 15% churn reduction, 35% support ticket deflection, 45% GenAI cost savings, and a shared inference library that cut deployment time from weeks to days.”
Sri Lekha Kandadai
Mid-level Machine Learning Engineer specializing in MLOps, monitoring, and multimodal AI
“ML/AI engineer focused on production-grade model reliability: built a monitoring and validation framework to detect drift, trigger anomaly alerts/retraining, and maintain consistent performance for device intelligence workflows at scale. Strong MLOps background with Python pipelines, Docker/Kubernetes deployments, Airflow orchestration, and real-time monitoring dashboards; experienced partnering with product managers to deliver business-facing insights.”
Yili Jin
Intern Software Engineer specializing in data systems and machine learning
“Internship experience at TikTok and nCino, with hands-on work spanning production Python data pipelines, recommendation-system feature workflows, Salesforce Apex automation, and flaky UI automation for a live stock recommendation platform. Stands out for a reliability-focused approach: anticipating failure modes, instrumenting observability, and turning ambiguous business processes into maintainable automated systems.”