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Vetted Pinecone Professionals
Pre-screened and vetted.
Senior AI/ML Engineer specializing in healthcare AI, LLMs, and MLOps
Senior Generative AI & Machine Learning Engineer specializing in LLMs and MLOps
Executive AI/CTO leader specializing in agentic LLM platforms and enterprise architecture
Senior Machine Learning Engineer specializing in LLMs, agentic AI, and MLOps
Senior Data Scientist specializing in ML search, recommendations, and generative AI
Junior Full-Stack Software Engineer specializing in ML, cloud infrastructure, and LLM agents
Mid-level Data Scientist / GenAI Engineer specializing in LLMs, RAG, and MLOps
Mid-level Software Engineer specializing in backend systems and LLM applications
Mid-level AI Backend Engineer specializing in LLM applications and scalable ML services
Mid-level AI/ML Engineer specializing in GenAI, RAG, and cloud-native ML platforms
Mid-level AI/ML Engineer specializing in NLP, RAG, and agentic AI
Mid-level AI Data Scientist specializing in financial risk, fraud detection, and NLP/LLM systems
Mid-level AI/ML Engineer specializing in MLOps, distributed ML, and RAG pipelines
Senior Machine Learning Engineer specializing in NLP, Generative AI, and healthcare/legal AI
Abhishikth Meesala
Mid-level AI/ML Engineer specializing in NLP, Generative AI, and fraud detection
“At PwC, built and productionized an agentic RAG enterprise search assistant over 6M internal documents (8M embeddings), deployed across AWS and GCP. Drove major retrieval gains (72%→92% precision via BM25+dense hybrid with RRF and cross-encoder re-ranking), reduced hallucinations 30%, achieved <2s latency at 50–60K queries/month, and cut support tickets 30%—boosting adoption to 2,500 users by adding source-cited answers.”
Syed Daim Ali
Intern Software Engineer specializing in FinTech and AI platforms
“Systems-focused engineer who built an OS kernel with multithreading, priority scheduling, system calls, and synchronization primitives, and debugged race conditions end-to-end. While not yet hands-on with ROS/SLAM, they clearly connect low-level concurrency and scheduling decisions to deterministic, reliable robotics-style real-time workloads.”