.png){kind=logo}
Vetted Logistic Regression Professionals
Pre-screened and vetted.
Mid-level Machine Learning/AI Engineer specializing in GenAI, RAG, and LLM inference
Mid-level QA/Systems Analyst specializing in test tooling, observability, and healthcare data migration
Mid-level Data Scientist / AI/ML Engineer specializing in financial services and GenAI
Mid-level AI/ML Engineer specializing in NLP, LLMs, and fraud/AML analytics
Mid-level Full-Stack Developer specializing in React/Node and cloud dashboards
Mid-level Machine Learning Engineer specializing in healthcare risk prediction and GenAI
Mid-level Machine Learning Engineer specializing in LLMs, RAG, and MLOps
Mid-level Data Scientist specializing in ML, NLP, and scalable data pipelines
Mid-level Data Scientist specializing in NLP, risk analytics, and MLOps
Mid-level Data Scientist specializing in ML, NLP, and forecasting across finance and retail
Junior Data & AI Analyst specializing in BI, LLM applications, and analytics
Mid-level AI/ML Engineer specializing in cloud MLOps, LLM agents, and risk & fraud modeling
Senior Data Scientist specializing in ML engineering and cloud analytics
Mid-level Data Scientist specializing in ML, NLP, and LLM-powered analytics
Rodolfo Lopez
Senior Math Educator transitioning to Data Science & Business Analytics
“Recent McCombs School of Business (UT Austin) Post Graduate Program graduate in Data Science & Business Analytics with hands-on project experience spanning stock clustering/segmentation and hotel booking-cancellation prediction. Strong in end-to-end analysis workflows (EDA, cleaning, feature engineering) and rigorous model comparison/selection, with exposure to boosting methods and imbalanced-data techniques; limited experience so far with embeddings/vector databases and production deployment.”
Sai Manikanta Kasireddy
Mid-level Machine Learning Engineer specializing in cloud-native GenAI and RAG systems
“Built and productionized an internal GenAI chatbot that makes company policy/SOP knowledge instantly searchable, using a secure RAG architecture on AWS (Bedrock/Titan embeddings/OpenSearch Serverless, Textract/Lambda/S3 ingestion, Claude 3 Sonnet). Demonstrates strong MLOps/orchestration experience (Airflow, Step Functions with Lambda/Glue/SageMaker) and a rigorous reliability approach (RAGAS metrics, A/B testing, citation validation, monitoring), including collaboration with compliance stakeholders via review dashboards.”
Sudheer koki
Mid-level AI/ML Engineer specializing in predictive modeling, data pipelines, and RAG systems
“Built and productionized an LLM-powered internal knowledge search system in a regulated environment, using embeddings/vector DB retrieval with strict grounding and confidence gating to reduce hallucinations. Reported ~45% accuracy improvement over keyword search and implemented end-to-end orchestration, monitoring, CI/CD, and incremental re-indexing to manage latency and data freshness while driving adoption with business stakeholders.”
Laxminarayana Yaga
Mid-level AI/ML Engineer specializing in Generative AI, RAG, and MLOps
“Built and deployed a production RAG pipeline at PNC Financial Services to let risk/compliance analysts query millions of internal financial documents in natural language, reducing manual search and speeding regulatory validation. Demonstrates deep practical experience with large-scale document ingestion/OCR cleanup, retrieval performance tuning (hierarchical indexing, caching), and LLM reliability controls (grounding, citations, abstention), plus cloud orchestration on Azure and AWS.”
Mid-level AI/ML Engineer specializing in financial risk, fraud detection, and NLP
Mid-level Data Scientist / Machine Learning Engineer specializing in NLP and computer vision