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Vetted Pinecone Professionals
Pre-screened and vetted.
Mid-level GenAI Engineer specializing in agentic workflows, RAG, and LLM orchestration
Mid-level Software Engineer specializing in AI/GenAI and cloud-native backend systems
Principal AI Architect & Data Engineer specializing in GenAI, agentic systems, and MLOps
Mid-level AI Engineer specializing in NLP, LLM fine-tuning, and RAG systems
Junior AI/ML Engineer specializing in RAG, multi-agent systems, and enterprise NLP
Mid-level Backend & AI Engineer specializing in agentic systems and scalable microservices
Mid-level Full-Stack Software Engineer specializing in cloud-native AI and enterprise platforms
Mid-level AI/ML Engineer specializing in Generative AI and cloud MLOps
Junior Machine Learning Engineer specializing in LLM agents, knowledge graphs, and multimodal AI
Mid-level AI/ML Engineer specializing in LLMs, RAG, and agentic AI systems
Mid-level AI Engineer specializing in LLM automation and RAG systems
Mid-Level Full-Stack Software Engineer specializing in cloud-native microservices and FinTech
Junior Machine Learning Engineer specializing in deep learning and healthcare AI
Junior AI/ML Engineer specializing in RAG and multi-agent LLM systems
Mid-level AI/ML Engineer specializing in MLOps, streaming data, and NLP/CV
Mid-level AI/ML Engineer specializing in GenAI, RAG, and multi-agent LLM systems
Ruthvika Mamidyala
Mid-level Data Scientist specializing in GenAI, RAG, and predictive modeling
“Backend engineer who built and evolved Python/FastAPI services (including AWS-deployed ML prediction APIs) for real-time profitability and risk insights at TenXengage. Emphasizes pragmatic architecture, strong validation/observability, and secure access controls (RBAC + row-level filtering), and has led safe migrations via parallel runs and incremental rollouts; reports ~20% forecasting accuracy improvement.”
Swarag Reddy Pingili
Junior AI/ML Software Engineer specializing in LLM agents and RAG systems
“AI/back-end engineer at Canon who helped build and operate an internal production LLM platform that acts as a secure middle layer between users and models, defending against jailbreaks/prompt injection while enabling RAG, memory, and grounded responses over company data. Experienced with LangChain/LangGraph orchestration, vector DB retrieval, and reliability practices (testing, monitoring, adversarial prompts) to run high-throughput, low-latency AI workflows in production.”
Phat Dang
Junior Software Engineer specializing in full-stack, DevOps, and GenAI
“Robotics software engineer with hands-on hardware integration who built an AI-enabled smart dog door using a Raspberry Pi, camera-based recognition (DeepFace adapted for dogs), and stepper motor control (TB6600/NEMA 17). Experienced in ROS/ROS 2 across perception-to-controls, rigorous bag-driven debugging of SLAM/navigation issues, and deploying robot software with simulation-in-the-loop testing plus Docker/Kubernetes CI/CD.”
Swapnil Ramanna
Mid-level Data Scientist specializing in Generative AI and Healthcare Analytics
“Built a LangGraph-based, tool-routing LLM chatbot to deliver fast, trustworthy investment-stock insights (including tariff impact) and deployed it to production on Snowflake after initially developing in Azure with AI Search and the Microsoft Agent Framework. Improved routing robustness by moving from LLM-based decisions to a deterministic router backed by schema-relationship graphs and YAML metadata, and ran the project iteratively with non-technical stakeholders over an 8-month engagement.”