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Vetted AI & Machine Learning Professionals in the Bay Area
Pre-screened and vetted in the Bay Area.
Popular Titles
Daksh Adhar
Junior Robotics & Reinforcement Learning Engineer specializing in dexterous manipulation
“Robotics software engineer (master’s student) who placed 3rd in the CMU VLA challenge and presented at IROS, building an LLM-powered language system (Gemini 2.5) for mobile-robot scene Q&A and language-based navigation. Hands-on ROS1/ROS2 experience including ros2_control + PILZ planning for a KUKA arm, plus simulation (Gazebo) and containerized submissions with Docker.”
Nishitha Thummala
Mid-level AI/ML Engineer specializing in LLMs, RAG, and scalable inference
“Backend/retrieval-focused engineer with production experience at Perplexity building a large-scale real-time Q&A system using retrieval-augmented generation, emphasizing low-latency, high-quality answers through ranking, context optimization, and caching. Also has orchestration experience from both product-facing LLM pipelines and large-scale infrastructure workflows at Meta, and has partnered with non-technical stakeholders to align AI trade-offs with business goals.”
Kowshika M
Mid-level AI/ML Engineer specializing in LLM fine-tuning, inference optimization, and AI safety
“AI/LLM engineer with production experience at NVIDIA, where they fine-tuned and deployed a financial-services chatbot and cut latency ~50% using TensorRT + NVIDIA Triton, scaling via Docker/Kubernetes. Also has consulting experience at Accenture delivering a predictive maintenance solution for a logistics network, bridging non-technical stakeholders with actionable dashboards.”
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
Director of Machine Learning specializing in GenAI platforms and enterprise AI/ML
Mid-level AI/ML Engineer specializing in LLMs, ranking systems, and MLOps
Entry-level Full-Stack Engineer specializing in AI and healthcare applications
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.”
Ritesh Somashekar
Mid-level AI & ML Engineer specializing in NLP, LLMs, and scalable ML systems
“AI/ML engineer with experience spanning Accenture healthcare NLP systems, academic research, and Apple on-device LLM integration. Stands out for owning regulated production pipelines end-to-end—from HIPAA-compliant clinical NLP and EHR integrations to incident prevention, experiment tracking, and optimized on-device inference with LLaMA 3.”
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