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Vetted Fraud Detection Professionals
Pre-screened and vetted.
Mid-level AI/ML Engineer specializing in NLP, fraud detection, and LLM applications
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 AI/ML Engineer specializing in risk modeling, NLP, and Generative AI
Mid-level Backend Software Engineer specializing in Java microservices and cloud-native systems
Mid-level Data Engineer specializing in cloud lakehouse and streaming analytics for financial services
Mid-level AI/ML Engineer specializing in cloud MLOps, LLM agents, and risk & fraud modeling
Mid-level Software Engineer specializing in AI-driven full-stack systems
Senior Backend Software Engineer specializing in FinTech and distributed systems
Mid-level Full-Stack Software Engineer specializing in healthcare and AI applications
Senior AI/ML Engineer specializing in MLOps and Generative AI (LLMs/RAG)
Mid-level Applied AI Engineer specializing in Generative AI and RAG systems
Mid-level Data Scientist specializing in ML, NLP, and LLM-powered analytics
Mid-level Java Full-Stack Developer specializing in cloud-native microservices
Bharath Reddy Nallu
Mid-level Machine Learning Engineer specializing in NLP and scalable MLOps
“Data/ML engineer in financial services (Northern Trust) who built a production RAG-based LLM system to connect structured transaction/portfolio data with unstructured market and internal documents for risk teams. Strong in end-to-end pipelines (AWS Glue/Airflow/PySpark), entity resolution, and taking models from prototype to reliable daily production with performance tuning (LoRA + TensorRT) and monitoring.”
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.”