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Vetted Deep Learning Professionals
Pre-screened and vetted.
Mid-level AI/ML Engineer specializing in NLP/LLMs and production ML systems
Junior Mechanical/Robotics Engineer specializing in controls, vehicle dynamics, and autonomy
Mid-level Data/Software Engineer specializing in healthcare and FinTech analytics
Mid-level Machine Learning Engineer specializing in NLP, recommender systems, and on-device ML
Mid-level Robotics & Computer Vision Engineer specializing in humanoid manipulation and RL
Mid-level AI/ML Engineer specializing in LLM fine-tuning and RAG systems
Principal Data Engineer specializing in cloud-native AI and data platforms
Executive Engineering Leader specializing in Physical AI, Robotics, and HW/SW/AI integration
Junior Machine Learning Researcher specializing in biomedical AI and systems
Intern Biomedical Data Scientist specializing in healthcare AI and LLM-based clinical NLP
Mid-level Data Engineer specializing in cloud-native big data pipelines and analytics
Senior Machine Learning Engineer specializing in LLMs and Generative AI
Junior Robotics Engineer specializing in autonomous systems and robot learning
Senior Data Scientist specializing in Generative AI and conversational AI
Mid-level Software Engineer specializing in AI infrastructure and machine learning
Rhutwij Tulankar
Engineering Manager and ML/Data Architect specializing in scalable data platforms and personalization
“Hands-on engineering manager at a marketing company leading a highly senior, distributed team (10 direct reports) while personally coding ~60–70% and owning end-to-end architecture across three interconnected products. Built agentic CRM automation and a reinforcement-learning-driven distribution layer for channel spend/bidding, with a strong focus on scalable design and observability (Prometheus/APM/logging) enabling frequent releases and few production incidents.”
Venu Dave
Junior Software Development Engineer specializing in backend data platforms and LLM applications
“Amazon internship experience building and shipping an end-to-end NL-to-SQL system: ingested/normalized metadata across 60+ internal tables, added rigorous multi-layer validation for LLM-generated SQL, and served it via a FastAPI backend for engineers—driving 90%+ faster dataset discovery and ~70% lower effort to access data. Also built an early-stage RAG-based healthcare assistant, iterating on chunking, embeddings, and retrieval to improve answer quality post-launch.”