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Vetted Data Scientists in Remote
Pre-screened and vetted in Remote.
Senior Data Scientist specializing in Generative AI and NLP
Mid-level Data Scientist specializing in NLP, MLOps, and cloud-deployed ML systems
Mid-level AI/ML Engineer specializing in NLP, LLMs, and MLOps
Mid-level AI/ML Engineer specializing in GenAI, computer vision, and real-time ML pipelines
Kranthi Kumar Karupati
Mid-level Generative AI Engineer specializing in LLM apps, RAG, and MLOps
“LLM/GenAI engineer with US Bank experience building a production financial-document intelligence platform using LangChain/LangGraph, GPT-4, and Amazon OpenSearch. Delivered a RAG-based assistant for compliance/audit teams with grounded, cited answers, focusing on reducing hallucinations and latency, and deployed securely on AWS (SageMaker/EKS) with CI/CD and evaluation tooling (LangSmith, RAGAS).”
“Built an AI-driven insurance policy summarization platform at Marsh, taking it end-to-end from messy PDF ingestion/OCR and custom extraction through LLM fine-tuning and AWS SageMaker deployment. Delivered measurable impact (25% reduction in manual review time, 99% uptime) and demonstrated strong production MLOps/LLMOps practices with Airflow/Step Functions orchestration, rigorous evaluation (ROUGE + human review), and continuous monitoring for drift, latency, and hallucinations.”
Mid-level AI/ML Engineer specializing in NLP, generative AI, and MLOps
Mid-level Data Scientist specializing in credit risk, NLP, and ML platforms
Senior Machine Learning Engineer specializing in LLM systems and big data infrastructure
Mid-level Data Scientist specializing in GenAI, LLMs, and MLOps
Mid-level Data Scientist specializing in AI/ML for finance and investment analytics
Mid-level Data Scientist specializing in LLMs, RAG, and ML systems for finance and healthcare
Mid-level Data Scientist / ML Engineer specializing in Generative AI and NLP
Mid-level Full-Stack Engineer specializing in AI, LLM/RAG platforms, and data systems
Mid-level AI/ML Engineer specializing in MLOps, Databricks Lakehouse, and GenAI RAG systems
Yurong Luo
Senior Data Scientist/ML Engineer specializing in scalable ML and LLM systems
“Built and deployed an end-to-end product that brings a research-paper approach into production for large-scale time-series clustering, with attention to partitioning, latency, and scalability. Also designed a Python-based backend validation service (comparing outputs to database ground truths) and handled production reliability issues by reproducing dataset-specific crashes and hardening corner-case behavior with client-friendly errors.”
Mid-level AI Engineer specializing in ML platforms, recommender systems, and GenAI/RAG
Sushmitha Ramesh
Mid-level Data Scientist specializing in ML, LLMs, and Azure MLOps
“Cloud/ML engineer with production deployment experience on Azure (Dockerized models, managed APIs, data pipelines) who has repeatedly stabilized unreliable systems—e.g., taking an API-driven analytics pipeline from ~60% to 98% reliability and an Azure ML service from ~80% to 97% by addressing rate limits, container memory, and gateway timeouts. Also built an explainable contract-risk model for entertainment bookings (Transformers + SHAP) and integrated it into a legacy booking system via a Flask REST API, plus prior IoT work at Nissan processing CAN bus sensor streams for diagnostics/anomaly insights.”
Ramya Konda
Mid-level AI/ML Engineer specializing in healthcare ML and generative AI
“AI/LLM engineer at Humana who built and deployed a HIPAA-aware RAG system for clinical record retrieval, cutting search time dramatically and improving retrieval efficiency by 30%. Experienced with Spark-scale data preprocessing, QLoRA fine-tuning, LangChain orchestration, and MLflow+SageMaker integration, with a strong testing/evaluation discipline (A/B tests, human eval) to hit 95%+ accuracy and production latency targets.”
Andrew Clayman
Senior Data Scientist specializing in ML, NLP, and production AI systems
“Machine learning/NLP engineer with deep Azure stack experience (Data Factory, Databricks/Spark, Delta Lake, Azure OpenAI, Azure AI Search) who built end-to-end production systems for semantic clustering, entity resolution, and hybrid search. Demonstrated measurable gains from embedding fine-tuning (~15% retrieval precision, ~10–12% nDCG@10) and designed scalable, quality-checked pipelines with MLOps best practices.”
Mid-level Data Scientist & AI/ML Engineer specializing in GenAI, NLP, and predictive modeling
Mid-level Data Scientist specializing in ML, NLP, and cloud data platforms
Mid-level Data Scientist specializing in MLOps, real-time ML, and generative AI (RAG/LLMs)