.png){kind=logo}
Vetted Unsupervised Learning Professionals
Pre-screened and vetted.
Mid-level Machine Learning Engineer specializing in MLOps, RAG, and real-time personalization
Staff Machine Learning Engineer specializing in LLMs, recommendations, and MLOps
Senior AI/ML Engineer specializing in Generative AI and LLM applications
Director-level Wireless Software Engineer specializing in 3GPP NAS protocol stacks (LTE/NR)
Mid-level AI/ML Engineer specializing in LLM RAG pipelines and cloud MLOps
Mid-level AI/ML Engineer specializing in GenAI agents and production ML systems
Mid-level Machine Learning Engineer specializing in MLOps and Generative AI
Mid-level Machine Learning Engineer specializing in fraud detection and recommendations
Mid-level Data Scientist specializing in GenAI, LLMs, and MLOps
Mid-level AI/ML Engineer specializing in cloud MLOps and GenAI for fraud detection
Ritika Ghosh
Junior Robotics & AI Engineer specializing in ROS2 autonomy and real-time computer vision
“Robotics software engineer from Stanley Black & Decker’s autonomous team who built and deployed a ROS2-based model predictive control system for a commercial autonomous lawn mower, integrating real-time localization, Nav2 planning, and custom control under real-time constraints. Has hands-on field debugging experience (Foxglove, TF timing, covariance/noise tuning) to resolve issues that only appeared outside simulation, plus containerized deployment and CI/CD experience.”
Jacqueline Zhang
Mid-level Machine Learning Engineer specializing in LLMs, fairness, and healthcare ML
“ML/NLP practitioner with a master’s thesis focused on domain-adaptive knowledge distillation for LLMs (LLaMA2/sheared LLaMA), showing improved perplexity and ROUGE-L on biomedical data. Also built real-world data linking and search systems: integrated ClinicalTrials.gov with FAERS using fuzzy matching + embeddings, and delivered an LLM-powered FAQ recommender at Hyperledger using sentence-transformers, FAISS, and fine-tuning to mitigate embedding drift.”
Derek Tuggle
Executive Robotics & Machine Learning Engineer specializing in industrial IoT controls
“VP of New Product Development at Axiom Cloud who built and scaled a "Virtual Battery" product that used supermarket frozen inventory as thermal energy storage—personally prototyped core control/safety logic in Python and led the engineering buildout through deployment and operations. Combines real-world industrial controls and edge deployment experience (LonWorks/Modbus, Docker/CI/CD) with an MS in CS focused on robotics, perception, and ML, including ROS 2 and YOLO-based perception.”
Vishal Mittal
Director-level Engineering Manager specializing in cloud security platforms and AI-driven automation
“Senior engineering leader in the Bay Area with experience spanning VMware, Hortonworks/Cloudera, Barracuda, and Palo Alto Networks, including leading open-source work (Apache Knox) and architecting large-scale security platforms. Has driven disaster recovery and cloud security products, designed Python microservices for Microsoft 365 security, and scaled teams (3x) while formalizing enterprise readiness practices with automated documentation using Notebook LLM.”
Chappidi Sasi
Mid-level Machine Learning Engineer specializing in GPU-accelerated LLM training and inference
“ML/LLM engineer with production experience building a multi-GPU LLM inference platform using TensorRT and vLLM, achieving ~40% p95 latency reduction through batching/KV caching, quantization, and CUDA/runtime tuning. Also has end-to-end orchestration experience (Kubernetes, Airflow) and has delivered real-time fraud detection systems at Accenture in close collaboration with non-technical risk and product stakeholders.”
Yash Jajoo
Senior Software Engineer specializing in AI and FinTech platforms
“Built a production LLM pipeline at Walter AI that scans massive user inboxes, identifies financial newsletters, and extracts trading strategies into structured JSON for downstream paper-trading workflows. Stands out for combining agent architecture with strong production discipline—cutting scan time from 20 to 5 minutes, reducing LLM costs by 90%, and achieving 3-second P99 latency while handling messy, inconsistent email data at scale.”
Mid-level Machine Learning Engineer specializing in deep learning, MLOps, and real-time inference
Mid-level AI/ML Engineer specializing in LLMs, MLOps, and recommendation systems
Senior AI/ML Software Engineer specializing in LLM and RAG systems
Junior Software Engineer specializing in healthcare data and LLM-powered workflows
Mid-level AI/ML Engineer specializing in Generative AI, LLMs, and GPU-accelerated deep learning