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Vetted Convolutional Neural Network (CNN) Professionals
Pre-screened and vetted.
Senior AI/ML Engineer specializing in LLMs, RAG, and multimodal recommendation systems
Mid-level AI/ML Engineer specializing in LLM fine-tuning, RAG, and MLOps
Senior AI/ML Engineer specializing in computer vision, NLP, and real-time forecasting
Intern Machine Learning Engineer specializing in LLM systems and recommendation/search
Mid-level AI/ML Engineer specializing in RAG, NLP, and MLOps
Parth Vadera
Senior AI/ML Data Scientist specializing in recommender systems, LLMs, and MLOps
“ML/NLP leader with 12+ years of impact across LinkedIn, TikTok, and Levi's, building and productionizing multimodal recommendation and embedding-based search systems. Deep experience in entity resolution, vector retrieval, and rigorous evaluation, with cloud-native deployment/monitoring (MLflow, Airflow, SageMaker/Lambda, Azure ML, Kubernetes) and demonstrated double-digit relevance gains at millions-of-users scale.”
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.”
Yishi Wang
Junior Machine Learning & Data Science professional specializing in LLMs and analytics
“Amazon internship experience building production GenAI analytics for the returns organization: a multi-agent LLM+RAG system that let analysts query multiple heterogeneous data sources in natural language without hand-written SQL. Also built and operationalized four Apache Airflow DAGs for large-scale ETL, emphasizing observability and freshness-aware metadata to keep outputs accurate and up to date.”
Principal Machine Learning Scientist specializing in GenAI, LLMs, and RAG
Senior Python Developer specializing in AI/ML and cloud-native microservices
Mid-level Software Engineer specializing in systems, CUDA, and robotics/AI
Mid-level Machine Learning Engineer specializing in generative AI, NLP, and MLOps
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.”
Prahlad Vivek
Intern Robotics Engineer specializing in robot learning, SLAM, and control
“Robotics architect intern/new-grad focused on warehouse AMRs, building ROS2 sensor-fusion and SLAM stacks (FastSLAM-style particle filter) and validating in Gazebo with ground-truth metrics. Also interned at ASML debugging real-time in-vacuum robot behavior via Python state-machine telemetry scripts, identifying a firmware driver issue impacting throughput.”
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.”
Krishna Sahith Poruri
Mid-level AI/ML Engineer specializing in LLMs, RAG, and MLOps
“ML/LLM engineer who built a production RAG system (GPT-4 + FAISS + FastAPI) to deliver fast, grounded answers from proprietary documents, optimizing for sub-200ms latency and high-concurrency scale. Strong MLOps/observability background: drift monitoring with Prometheus + Streamlit, automated retraining via Airflow, Kubernetes autoscaling, and MLflow-managed model lifecycle, plus inference cost reduction through quantization and structured pruning.”
Mid-level AI/ML Engineer specializing in NLP, computer vision, and MLOps
Mid-level AI/ML Engineer specializing in recommender systems, fraud detection, and LLMs
Executive engineering leader specializing in capital markets, risk systems, and FinTech
Mid-level Machine Learning Engineer specializing in NLP, recommender systems, and on-device ML
Mid-level AI/ML Engineer specializing in GPU-accelerated LLM and vision systems