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Vetted AI & Machine Learning Professionals
Pre-screened and vetted.
Popular Titles
Senior AI/ML Engineer specializing in applied AI and scalable backend systems
Senior Machine Learning Engineer specializing in recommender systems, search, and NLP/GenAI
Executive Technology Leader specializing in GenAI, cloud infrastructure transformation, and enterprise modernization
Senior AI Engineer specializing in LLMs, generative AI, and ML systems
Staff Machine Learning Engineer specializing in LLMs and Generative AI
Senior Software Engineer specializing in Generative AI and distributed systems
Senior Machine Learning Engineer specializing in large-scale AI systems
Senior AI Engineer specializing in LLMs, RAG, and production ML systems
Staff AI Systems Engineer specializing in LLM platforms and distributed systems
Mid-level AI/ML Engineer specializing in LLM training, RAG, and scalable inference
Staff Machine Learning Engineer specializing in search, ranking, and LLM systems
Senior AI Engineer specializing in NLP and large language models
Richard Jackson
Senior Machine Learning Engineer & Solution Architect specializing in cloud AI systems
“Backend/ML platform engineer with Google experience leading Python microservices for an AI-driven recommendation/retrieval system, including PyTorch inference and a retrieval-augmented generation workflow. Strong in production Kubernetes + GitOps (ArgoCD), real-time Kafka/Spark pipelines, and phased on-prem/legacy to AWS/GCP cloud migrations with reliability-focused rollout and rollback practices.”
Senior AI/ML Engineer specializing in LLMs, recommendation systems, and ML platforms
Senior AI/ML Engineer specializing in LLM applications, RAG systems, and MLOps
Staff Machine Learning Scientist specializing in NLP, LLMs, and Generative AI
Staff AI/ML Engineer specializing in LLMs, fraud detection, and MLOps
Senior Machine Learning Engineer specializing in Generative AI and NLP
Chase Bradley
Staff AI Engineer specializing in LLM systems, retrieval, and ML infrastructure
“ML/LLM engineer from Cohere who has owned retrieval, reranking, agentic workflows, and internal evaluation infrastructure end-to-end in production. Particularly strong in turning brittle RAG and research-heavy ideas into scalable enterprise systems with grounded outputs, lower customer escalations, and adoption by major clients like Notion and Fujitsu.”
Christopher Sipola
Senior Machine Learning Engineer specializing in LLMs and recommendation systems
“ML/GenAI engineer who owned major parts of Spotify’s AI DJ from offline experimentation through deployment, monitoring, and iteration. They combine recommender systems, RAG, real-time feedback loops, and LLM safety/orchestration to ship consumer-facing personalization features that drove double-digit engagement and deeper listening sessions.”
Esha Pahwa
Intern Machine Learning Engineer specializing in LLM agents and multimodal reasoning
“LLM/agent engineer who built a production code-generation agent at Corvic AI that lets non-technical users query CSV/tabular data in natural language by generating and executing Python. Focused on making LLM systems reliable and scalable via schema-aware validation, sandboxed execution-feedback retries, prompt caching/embeddings, async execution, and high-throughput data processing with Polars; also partnered with Adobe product/marketing to ship brand-aligned AI content generation for email and push notifications.”
Sudheer Bommichetty
Mid-level Machine Learning Engineer specializing in NLP, MLOps, and Generative AI
“Built and deployed a production LLM conversational AI system at OpenAI supporting chat, summarization, and semantic search at 1M+ requests/day, driving major latency (40%) and accuracy (25%) improvements through Pinecone optimization and tighter RAG with re-ranking. Also has Amazon experience improving recommendation systems by translating ML metrics into business terms to boost CTR and conversions, with strong MLOps/orchestration depth (Airflow, MLflow, SageMaker, Kubeflow).”
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