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Vetted Large Language Models Professionals
Pre-screened and vetted.
Mid-level Machine Learning Engineer specializing in LLM inference and MLOps
Mid-level Machine Learning Engineer specializing in LLMs, RAG, and MLOps
Junior Software Engineer specializing in full-stack and machine learning systems
Mid-Level Software Engineer specializing in cloud-native microservices and AI/ML
Intern AI Researcher specializing in NLP, multimodal generative AI, and medical imaging
Mid-level Machine Learning Engineer specializing in LLMs, RAG, and MLOps
Senior Data Scientist specializing in Generative AI/NLP for legal and healthcare domains
Mid-level AI/ML Engineer specializing in LLM evaluation, RAG, and GPU-accelerated inference
Junior AI/ML Engineer specializing in NLP, LLM evaluation, and Azure MLOps
Senior Software Engineer specializing in Python, AI/ML, and AWS cloud-native systems
Mid-level AI/ML Data Engineer specializing in data pipelines, MLOps, and LLM/RAG systems
Senior Data Analytics & Applied ML Engineer specializing in LLMs, RAG, and MLOps
Mid-level AI/ML Engineer specializing in multimodal and generative AI at scale
Mid-level Software Development Engineer specializing in backend systems and ML platforms
Peeyush Dyavarashetty
Intern AI/ML Engineer specializing in GenAI, LLMs, and agentic RAG systems
“AI/LLM practitioner who built a GPT-2-like language model from scratch at the University of Maryland using PyTorch and multi-GPU distributed training, with experiment tracking in Weights & Biases. As an AI Operations intern at ScaleUp360, delivered multiple production-style AI agent automations (Gmail classification and Fireflies-to-Claude workflows that extract and assign CEO tasks) and set up measurable evaluation using test cases and classification metrics.”
Pavan Devulapalle
Mid-level Software Engineer specializing in cloud platforms and AI-integrated full-stack development
“Backend engineer who built Flask-based internal APIs supporting GenAI-driven provisioning/diagnostics (Outpost/AWS Outposts-like environment), with deep hands-on optimization across Postgres/SQLAlchemy (2s to <200ms endpoint improvement). Experienced integrating ML/LLM workflows via AWS SageMaker and Bedrock, and designing multi-tenant isolation plus high-throughput Redis-backed background task pipelines (minutes to seconds).”
Gagan Mundada
Intern Machine Learning Engineer specializing in multimodal AI and evaluation benchmarks
“ML-focused candidate with beginner ROS/ROS2 experience (custom pub-sub nodes; TurtleBot3 SLAM simulation debugging via topic inspection and transform/orientation checks). Has research/project exposure to LLM training approaches (GRPO with pseudo-labels using Hugging Face TRL on Qwen/Llama) and uses Docker/Kubernetes + CI/CD to run ViT saliency-attention/compression workloads on UCSD Nautilus infrastructure.”