Pre-screened and vetted in the DFW Metroplex.
Principal AI Architect specializing in GenAI, agentic systems, and RAG
Mid-level AI/ML Engineer specializing in NLP, RAG, and MLOps
“Built a production LLM/RAG-based “model excellence scoring” system at Uber to automatically evaluate hundreds of ML models, standardizing quality assessment and cutting evaluation time from days to minutes on GCP. Also delivered an NLP document classification solution for insurance claims at Globe Life, partnering closely with compliance/operations and improving routing accuracy from ~85% manual to 93% with the model.”
Mid-level Applied AI Engineer specializing in GenAI and financial NLP
Senior Data Scientist specializing in AI agents and LLM production systems
Mid-level AI Engineer specializing in LLM agents, RAG, and knowledge graphs
Mid-level Data Scientist / AI-ML Engineer specializing in Generative AI and LLM applications
“Built a production GenAI-powered analytics assistant to reduce reliance on data analysts by enabling natural-language Q&A over Databricks/Power BI dashboards, backed by vector search (Pinecone/Milvus) and a Neo4j knowledge graph, including multimodal support via OpenAI Vision. Demonstrates strong real-world LLM reliability engineering with strict RAG, LangGraph multi-step verification, and Guardrails/custom validators, plus broad orchestration and production monitoring experience (Airflow, ADF, Step Functions, Kubernetes, Prometheus/CloudWatch).”
Mid-level AI/ML Engineer specializing in GenAI, NLP, and MLOps
“GenAI/data engineering practitioner with production experience across Equinix, Optum, and Citibank—built an Azure OpenAI (GPT-4) + LangChain document intelligence platform processing 1.5M+ docs/month and a HIPAA-compliant Airflow healthcare pipeline handling 5M+ claims/day. Also delivered a real-time fraud detection + explainability system using LightGBM and a fine-tuned T5 NLG component, improving fraud accuracy by 15%+ while partnering closely with compliance stakeholders.”
Senior Data Scientist specializing in GenAI, LLMs and RAG
“Built and deployed a production LLM-powered RAG assistant for semiconductor manufacturing failure analysis, reducing engineer triage effort by grounding outputs in retrieved evidence and gating responses with SPC + ML signals (LSTM anomaly scores, XGBoost probabilities). Experienced with LangChain/LangGraph to ship reliable, observable multi-step agents with branching/fallback logic, and evaluates impact using both technical metrics and business KPIs like mean time to triage and downtime reduction.”
Mid-level Data Scientist specializing in LLMs, RAG systems, and production MLOps
Mid-level Data Analyst/Data Scientist specializing in product analytics and machine learning
Mid-level Data Scientist specializing in financial ML, forecasting, and NLP/GenAI
Mid-level Data Scientist specializing in ML, MLOps, and LLM fine-tuning
Mid-level AI/ML Engineer specializing in MLOps, RAG, and production NLP
Senior Data Scientist specializing in ML, analytics, and cloud data platforms
Mid-level Data Scientist specializing in LLMs, RAG systems, and production MLOps
Mid-level Data Scientist specializing in fraud detection and scalable ML systems
Senior Data Scientist specializing in LLM-powered NLP, recommender systems, and anomaly detection
Mid-level AI/ML Engineer specializing in GenAI, NLP, and MLOps
Senior Data Scientist / ML Engineer specializing in NLP and Generative AI
Mid-level Generative AI Engineer specializing in LLMs, RAG, and agentic AI
Senior Data Scientist specializing in ML, fraud risk, and Generative AI (RAG/LLMs)
Senior ML Engineer & Data Scientist specializing in LLM agents, retrieval/ranking, and MLOps
“Machine Learning Engineer currently at Webster Bank building an enterprise-scale LLM agent for Temenos Journey Manager/Maestro, using RAG-style multi-stage retrieval with FAISS/Pinecone, hybrid dense+sparse search, and LoRA fine-tuning optimized via NDCG/MAP and A/B testing. Previously handled messy incident/telemetry data at Deuta Werke GmbH with deterministic + fuzzy entity resolution, and has strong production data engineering experience across Spark/Hadoop and Python ETL systems.”
Junior Data Scientist specializing in fraud analytics and cloud data platforms
“Built and deployed production LLM-powered document summarization/classification systems using embeddings, vector databases (RAG-style retrieval), and automated evaluation (BERTScore/ROUGE), with a focus on monitoring and scalable cloud pipelines. Also partnered with a fraud analytics team to deliver a transaction anomaly detection solution, translating model outputs into Power BI dashboards and actionable KPIs while iterating on thresholds and alerts based on stakeholder feedback.”
Mid-level Machine Learning Engineer specializing in LLM agents, RAG, and MLOps
“Built production LLM systems including a real-time customer feedback analysis and workflow automation platform using RAG and multi-agent orchestration with confidence-based human escalation, addressing privacy and legacy integration challenges. Also automated ML operations with Airflow/Kubernetes (e.g., daily churn model retraining) cutting retraining time to under 30 minutes, and demonstrates a rigorous testing/monitoring approach plus strong non-technical stakeholder collaboration.”