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Vetted Amazon SageMaker Professionals
Pre-screened and vetted.
Mid-level AI/ML Engineer specializing in LLMs, NLP, and MLOps
Mid-level GenAI/MLOps Engineer specializing in banking and healthcare LLM applications
Mid-level Machine Learning Engineer specializing in MLOps, fraud detection, and data security
Mid-level Machine Learning & AI Engineer specializing in LLMOps, digital twins, and RL
Mid-level Data Scientist specializing in machine learning and analytics
Mid-level Machine Learning Engineer specializing in MLOps and Generative AI
Mid-level AI/ML Engineer specializing in LLM fine-tuning and RAG for healthcare
Junior AI Engineer specializing in distributed ML pipelines and time-series forecasting
Mid-level Data Science & AI/ML Engineer specializing in MLOps, NLP, and computer vision
Mid-level AI Engineer specializing in retail personalization and LLM-powered systems
Mid-level Data Scientist specializing in ML, NLP, and LLM-powered analytics
Junior Machine Learning Engineer specializing in healthcare AI and GenAI RAG
Mid-level Machine Learning Engineer specializing in production ML, MLOps, and Generative AI
Adithya Gururaj
Junior AI Engineer specializing in agentic AI, RAG, and voice/telephony systems
“LLM/agent engineer who has built production multi-agent systems (LangChain/LangGraph) for enterprise workflows like email and calendar automation, with a strong focus on latency, tool-calling accuracy, and evaluation via LangSmith. Also worked on AI long-term memory using knowledge graphs at VEAI and communicated the approach and tradeoffs to CEO/CTO stakeholders.”
sai anuragh Sangoju
Mid-level AI/ML Engineer specializing in fraud detection, credit risk, and NLP
“Built and deployed a production LLM-powered university support chatbot on Azure using a RAG pipeline, focusing on reducing hallucinations, improving latency, and handling ambiguous queries via confidence checks and clarification prompts. Also has hands-on orchestration experience (Airflow/Azure Data Factory), including hardening a demand-forecasting ingestion workflow with sensors, retries, and automated alerts, and uses a metrics-driven testing/monitoring approach for reliable AI agents.”