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Vetted PyTorch Professionals
Pre-screened and vetted.
Mid-Level Software Engineer specializing in Ads Serving and Machine Learning Systems
Senior Data Scientist specializing in large-scale ML systems and recommendations
Mid-level AI/ML Engineer specializing in NLP, computer vision, and recommender systems
Brian Sanders
Senior Backend Engineer specializing in GenAI, LLMs, and scalable data pipelines
“Backend/ML platform engineer from Snapsheet who owned production Python services and data pipelines for insurance claims, including an AI document classification/summarization FastAPI service on ECS/Fargate processing 1M+ documents/year. Strong in AWS infrastructure (Terraform, CI/CD, secrets/IAM, autoscaling), Glue/PySpark ETL with schema evolution controls, and legacy SAS-to-microservices modernization with safe, feature-flagged rollouts and measurable performance wins.”
Akhil Maram
Senior Data Engineer specializing in real-time data platforms and lakehouse architectures
“Senior, product-focused engineer who has built real-time customer-facing web applications and a microservices backend (TypeScript/React/Node) using RabbitMQ, MongoDB, and Redis. Demonstrates strong operational maturity (idempotency, tracing/observability, backpressure) and built an internal console that became the primary tool for debugging, replaying jobs, and managing system behavior.”
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.”
Mid-Level Software Engineer specializing in cloud-native backend systems and FinTech
Mid-level AI/ML Engineer specializing in LLMs, RAG, and distributed MLOps
Executive Full-Stack Developer specializing in computer graphics and AI-powered visual search
Mid-level Software Engineer specializing in distributed systems and big data
Mid-level Machine Learning Engineer specializing in LLMs, RAG, and scalable GPU inference
Intern Machine Learning Engineer specializing in LLMs, RAG, and model quantization
Senior Full-Stack Engineer specializing in AI-powered SaaS and cloud-native analytics
Senior Full-Stack AI/ML Engineer specializing in cloud data platforms and GenAI
Principal Data Scientist specializing in ML, NLP, and forecasting for marketing and supply chain
Principal Machine Learning Scientist specializing in GenAI, LLMs, and RAG
Mid-level Data Scientist specializing in ML for healthcare and strategy analytics
Mid-level AI/ML Engineer specializing in LLMs, RAG, and production MLOps