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Vetted Pinecone Professionals
Pre-screened and vetted.
Senior AI/ML Software Engineer specializing in LLMs, NLP, and scalable ML platforms
Intern AI/ML Engineer specializing in LLM agents, RAG, and low-latency systems
Senior Full-Stack AI Engineer specializing in cloud-native and enterprise AI systems
Senior Full-Stack Software Engineer specializing in cloud platforms and AI data systems
Mid-level Data Scientist / GenAI & ML Engineer specializing in LLMs, RAG, and recommendations
Intern Software Engineer specializing in data engineering and LLM evaluation
Senior AI/ML Engineer & Data Scientist specializing in NLP, entity resolution, and knowledge graphs
Staff Software Engineer specializing in AI, distributed systems, and data infrastructure
Staff Full-Stack & AI Engineer specializing in LLM platforms and scalable cloud systems
Senior AI/ML Engineer specializing in LLMs, RAG, and multimodal recommendation systems
Mid-level Machine Learning Engineer specializing in LLM personalization and scalable MLOps
Staff Software Engineer specializing in streaming platforms and AI-driven experiences
Staff Machine Learning Engineer specializing in LLMs and cloud-native AI platforms
Mid-level AI/ML Engineer specializing in LLMs, RAG, and multi-agent systems
Principal AI Infrastructure Engineer specializing in distributed systems and streaming platforms
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.”
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.”