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Vetted AI & Machine Learning Professionals in the Bay Area
Pre-screened and vetted in the Bay Area.
Popular Titles
Nikhil Reddy
Mid-level AI/ML Engineer specializing in GPU inference and LLM platforms
“Built and deployed an LLM-powered platform that turns models into scalable REST/gRPC APIs, focusing on keeping GPU-backed inference fast and stable during traffic spikes. Experienced with AWS orchestration (EKS/ECS/Step Functions), safe model rollouts, and production-grade monitoring/testing for reliable AI agents and workflows.”
Mid-level AI/ML Engineer specializing in NLP, computer vision, and MLOps
Mid-level Machine Learning Engineer specializing in LLMs and RAG systems
Intern AI/ML Engineer specializing in LLM agents, RAG, and computer vision
Mid-level Robotics & Computer Vision Engineer specializing in humanoid manipulation and RL
Mid-level AI/ML Engineer specializing in GPU-accelerated LLM and vision systems
Mid-level AI/ML Engineer specializing in LLM fine-tuning and RAG systems
Intern Software Engineer specializing in AI/ML and LLM retrieval systems
Mid-level Machine Learning Engineer specializing in LLMs, RAG, and GPU-accelerated cloud systems
Mid-level Human-Robot Interaction researcher specializing in multimodal social robotics
Mid-level AI/ML Engineer specializing in LLMs, ranking systems, and MLOps
Gowri Kajipuram
Mid-level AI/ML Engineer specializing in LLMs, RAG, and multimodal deep learning
“ML/LLM engineer who has built and productionized a large multimodal LLM pipeline end-to-end—fine-tuning a 20B+ parameter model with distributed/FSDP training and deploying on Kubernetes via Triton for ~5x throughput. Strong focus on reliability and safety (monitoring with SHAP, guardrails, A/B testing) with reported ~22% relevance lift and reduced harmful/incorrect outputs, plus experience orchestrating ETL/retraining workflows with Airflow across S3/Snowflake/RDS.”
Patrick Jose
Senior AI Engineer specializing in LLM applications and full-stack systems
“Built and owned a production LLM/RAG customer support assistant end-to-end, from prototype through deployment, monitoring, and iteration. Their work automated roughly 40% of common support queries and cut response times by about 30%, while also creating reusable Python inference services that improved consistency and team velocity.”
Chandra sai kiran Kammari
Mid-level Machine Learning Engineer specializing in fraud detection and real-time personalization
“ML/LLM engineer with Stripe and Adobe experience who productionized a transformer-based Payments Foundation Model for real-time fraud detection at global scale (billions of transactions). Built petabyte-scale ETL/feature pipelines (Spark/EMR, Airflow, dbt, Kafka/Flink) and achieved <100ms multi-region inference (EKS, TorchServe, edge/Lambda, GPU/CPU routing) with strong PCI-DSS/GDPR compliance and explainability (SHAP/LIME), reporting a 64% fraud accuracy improvement.”
Senior Machine Learning Engineer specializing in NLP, LLMs, and scalable ML platforms
Mid-level AI/ML Engineer specializing in LLMs, RAG pipelines, and MLOps
Mid-level Machine Learning Engineer specializing in real-time fraud detection and edge AI
Director of AI/ML specializing in edge AI, computer vision, and foundation models
Mid-level Machine Learning Engineer specializing in reinforcement learning and multimodal AI
Junior AI Prompt Engineer specializing in LLMs, RAG, and conversational AI
Senior AI/ML Engineer specializing in Generative AI and LLM applications
Intern Computer Vision/ML Engineer specializing in mapping, localization, and scalable inference