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Vetted Model Deployment Professionals
Pre-screened and vetted.
Executive Engineering Leader (VP/CTO) specializing in cloud-native platforms and AI/ML
Intern Machine Learning & Cloud Engineer specializing in cloud-native deployment and forecasting
Mid-level Machine Learning Engineer specializing in MLOps and cloud-native ML systems
Senior Data Scientist specializing in Generative AI and LLM evaluation
Senior Applied ML Scientist specializing in LLMs, ads ranking, and RAG systems
Mid-level Machine Learning Engineer specializing in LLMs, ranking, and scalable ML systems
Mid-level Data Engineer specializing in AI/ML and cloud data platforms
Senior AI/ML Engineer specializing in LLMs, RAG, and multimodal recommendation systems
Mid-level Machine Learning Engineer specializing in LLM personalization and scalable MLOps
Staff Machine Learning Engineer specializing in LLMs and cloud-native AI platforms
Senior AI/ML Engineer specializing in computer vision, NLP, and real-time forecasting
Brian Sanders
Senior Backend Engineer specializing in GenAI, LLMs, and scalable data pipelines
“Backend/ML platform engineer from Snapsheet who owned production Python services and data pipelines for insurance claims, including an AI document classification/summarization FastAPI service on ECS/Fargate processing 1M+ documents/year. Strong in AWS infrastructure (Terraform, CI/CD, secrets/IAM, autoscaling), Glue/PySpark ETL with schema evolution controls, and legacy SAS-to-microservices modernization with safe, feature-flagged rollouts and measurable performance wins.”
Senior Full-Stack Engineer specializing in AI-powered SaaS and cloud-native analytics
Principal Machine Learning Scientist specializing in GenAI, LLMs, and RAG
Mid-level AI/ML Engineer specializing in LLMs, RAG, and production MLOps
Senior Python Developer specializing in AI/ML and cloud-native microservices
Executive AI/ML Cloud Architect specializing in enterprise and humanitarian AI systems
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.”
Yuxin Xiong
Intern Machine Learning Engineer specializing in LLM reasoning, agents, and deployment
“AWS AI Lab engineer who deployed a production Chain-of-Thought analytical agent for tabular reasoning, emphasizing grounded tool-constrained workflows with schema-validated intermediate outputs. Built robust evaluation/logging with step-level observability to catch regressions across model versions, and has experience scaling distributed LLM training via Slurm + DeepSpeed/FSDP with checkpointing and failure recovery.”