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Vetted Supervised Learning Professionals
Pre-screened and vetted.
Mid-level AI/ML Engineer specializing in LLMs, RAG pipelines, and multi-agent systems
Senior AI/ML Software Engineer specializing in LLMs, NLP, and scalable ML platforms
Intern Software Engineer specializing in data engineering and LLM evaluation
Mid-level Machine Learning Engineer specializing in LLMs, ranking, and scalable ML systems
Mid-level Machine Learning Engineer specializing in LLM personalization and scalable MLOps
Mid-level AI/ML Engineer specializing in LLM fine-tuning, RAG, and MLOps
Mid-level Machine Learning Engineer specializing in real-time recommender systems and MLOps
Senior Data Scientist specializing in large-scale ML systems and recommendations
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
Principal Machine Learning Scientist specializing in GenAI, LLMs, and RAG
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
Qiang lu
Senior Robotics & Embodied AI Engineer specializing in closed-loop perception-to-action systems
“Robotics software engineer who built the behavior-tree orchestrator for the Vulcan Stow robotic system, migrating from a state machine to significantly improve testability. Experienced with ROS 1 and Baidu Apollo workflows (rosbag, LiDAR/image extraction) from self-driving simulation work at LG Silicon Valley Lab, and currently focused on stable Docker/docker-compose-based deployments with disciplined QA and hotfix processes.”
jiawei Li
Intern Applied Scientist specializing in LLM agents for software engineering
“Applied Scientist intern at Amazon who built a production-adopted LLM-judge to evaluate an agentic chatbot’s intermediate reasoning and tool calls using a knowledge-graph grounding approach. Also published award-winning work (ACM SIGSOFT Distinguished Paper) using LangChain + GPT-4 tools to generate factually grounded commit messages, with rigorous human-centered evaluation metrics.”
Mid-level AI/ML Engineer specializing in recommender systems, fraud detection, and LLMs
Senior AI/ML Engineer specializing in personalization, recommendations, and forecasting
Mid-Level Software Engineer specializing in full-stack development, cloud, and data infrastructure
Director-level Software Engineering Leader specializing in cloud platforms and large-scale systems
Mid-level AI/ML Engineer specializing in LLMs, ranking systems, and MLOps
Gowri Kajipuram
Mid-level AI/ML Engineer specializing in LLMs, RAG, and multimodal deep learning
“ML/LLM engineer who has built and productionized a large multimodal LLM pipeline end-to-end—fine-tuning a 20B+ parameter model with distributed/FSDP training and deploying on Kubernetes via Triton for ~5x throughput. Strong focus on reliability and safety (monitoring with SHAP, guardrails, A/B testing) with reported ~22% relevance lift and reduced harmful/incorrect outputs, plus experience orchestrating ETL/retraining workflows with Airflow across S3/Snowflake/RDS.”