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Vetted Unsupervised Learning Professionals
Pre-screened and vetted.
Principal Data Scientist & AI/ML Engineer specializing in LLMs, recommender systems, and MLOps
Mid-level AI/ML Engineer specializing in LLM training, RAG, and scalable inference
Staff Data Scientist specializing in machine learning, deep learning, and big data
Pradeep Nadella
Mid-level AI/ML Engineer specializing in LLM optimization and real-time fraud/risk modeling
“ML engineer with 5 years at Stripe building and productionizing real-time fraud detection at massive scale (3M+ transactions/day; $5B+ annual payment volume). Delivered measurable impact (22% accuracy lift, 18% loss reduction, +3–5% authorization rates) and has strong MLOps/orchestration experience (Docker, Kubernetes, Airflow, MLflow, CI/CD, monitoring/rollback) plus a structured approach to LLM agent/RAG evaluation.”
Mid-level AI/ML Engineer specializing in LLM training, RAG, and scalable inference
Senior AI/ML Engineering Manager specializing in NLP, computer vision, and MLOps
Mid-level Data Scientist specializing in Generative AI and LLM applications
Principal Data Scientist specializing in Generative AI and security analytics
Jayaprakash Majji
Mid-level AI/ML Engineer specializing in LLM fine-tuning, RAG, and scalable inference
“ML/LLM engineer who built and shipped an LLM-powered internal knowledge assistant at Meta, focusing on production-grade RAG to reduce hallucinations and improve trust. Deep experience with scaling and serving (FSDP/DeepSpeed/LoRA, Triton, Kubernetes autoscaling) and reliability practices (Airflow retraining, MLflow versioning, monitoring with rollback), including sub-100ms latency and ~35% GPU memory reduction.”
Brian Gomez
Senior Machine Learning Engineer specializing in AI/ML, NLP, and computer vision
“McKinsey & Company ML/NLP practitioner who builds production-grade AI systems across sectors (notably healthcare and finance), including RAG/LLM solutions, entity resolution pipelines, and embedding-powered search with vector databases. Demonstrated measurable impact (40% reduction in data duplication) and strong MLOps/data workflow practices (Airflow, MLflow, Spark, AWS/GCP, Prometheus, CI/CD).”
Deepak Guntreddi
Mid-level Full-Stack Developer specializing in Java/Spring Boot and React
“NVIDIA engineer who built and shipped a production LLM-powered enterprise knowledge system (summarization, transcription, and Q&A) that cut document retrieval time ~30%. Deep hands-on experience with RAG (FAISS/Pinecone), GPU-accelerated microservices on AWS, and reliability/safety practices (Guardrails AI, prompt A/B testing, canary releases) plus strong MLOps orchestration across Airflow, Step Functions, and Kubernetes GitOps.”
Senior Machine Learning Engineer specializing in LLMs and scalable MLOps
Senior Research Scientist specializing in LLM verification and fraud/risk modeling
Mid-level Data Scientist / GenAI & ML Engineer specializing in LLM apps and MLOps
Mid-level Machine Learning Engineer specializing in LLMs, RAG, and MLOps
Mid-level AI/ML Engineer specializing in LLMs, RAG, and scalable MLOps
Senior AI/ML Engineer specializing in LLM agents, RAG, and production ML systems
Mid-level Machine Learning Engineer specializing in LLMs, RAG, and MLOps