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Vetted Machine Learning Engineers in the Bay Area
Pre-screened and vetted in the Bay Area.
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
Junior Software Engineer specializing in LLMs, Agentic AI, and MLOps
Mid-level Machine Learning Engineer specializing in reinforcement learning and multimodal AI
Mid-level AI/ML Engineer specializing in LLM RAG pipelines and cloud MLOps
Mid-level AI/ML Engineer specializing in LLM, RAG, and multimodal systems
Mid-level Machine Learning Engineer specializing in fraud detection and recommendations
Chanakya rudru
Senior Machine Learning Engineer specializing in conversational AI and Generative AI
“ML/AI engineer with experience at Uber and Scale AI, focused on customer service automation across both classical NLP and generative AI systems. Has owned systems from experimentation through production on AWS, including LLM fine-tuning, RAG optimization, safety evaluation, and internal Python platform tooling that improved consistency and engineering velocity.”
Chappidi Sasi
Mid-level Machine Learning Engineer specializing in GPU-accelerated LLM training and inference
“ML/LLM engineer with production experience building a multi-GPU LLM inference platform using TensorRT and vLLM, achieving ~40% p95 latency reduction through batching/KV caching, quantization, and CUDA/runtime tuning. Also has end-to-end orchestration experience (Kubernetes, Airflow) and has delivered real-time fraud detection systems at Accenture in close collaboration with non-technical risk and product stakeholders.”
Mid-level Machine Learning Engineer specializing in LLMs, RAG, and MLOps
Mid-level Machine Learning Engineer specializing in LLMs, RAG, and MLOps
Mid-level Machine Learning Engineer specializing in fraud prevention and LLM systems
Mid-level Machine Learning Engineer specializing in LLMs, RAG, and MLOps
Mid-level AI/ML Engineer specializing in multimodal and generative AI at scale
Junior AI/ML Engineer specializing in agentic AI and cloud optimization
Mid-level AI/ML Engineer specializing in recommendation, retrieval, and MLOps
Mid AI/ML Engineer specializing in LLM systems and inference optimization
Mid-Level Software Development Engineer specializing in AWS edge AI and generative AI apps
Sairaghav Nissankula
Senior AI/ML Engineer specializing in LLMs, NLP, and enterprise conversational AI
“ML/GenAI engineer with strong end-to-end production ownership across predictive ML, RAG systems, and LLM routing. They pair solid platform engineering skills with measurable business impact, including 15% churn reduction, 35% support ticket deflection, 45% GenAI cost savings, and a shared inference library that cut deployment time from weeks to days.”
Mid-level Machine Learning & Data Engineer specializing in MLOps and cloud data platforms
Mid-level Machine Learning Engineer specializing in optimization, RL, and graph neural networks
Vismay Patel
Senior AI & Machine Learning Engineer specializing in NLP, GenAI, and MLOps
“ML/GenAI practitioner with healthcare domain depth who built and deployed a production cervical-cancer EMR classification system using a hybrid rules + medical BERT approach, optimized for high recall under severe class imbalance and PHI constraints. Experienced running end-to-end production ML/LLM pipelines with Apache Airflow (validation, promotion/rollback, monitoring, retraining) and partnering closely with clinicians to calibrate thresholds and implement human-in-the-loop review.”