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Vetted Machine Learning Engineers in the DFW Metroplex
Pre-screened and vetted in the DFW Metroplex.
Mid-level Machine Learning Engineer specializing in LLMs, RAG, and MLOps
Mid-level AI/ML Engineer specializing in RAG, NLP, and MLOps
MARCUS JOHNSON
Senior AI/ML Engineer specializing in Generative AI, NLP, and RAG systems
“ML/NLP engineer focused on production-grade data and search/recommendation systems: built an end-to-end pipeline that connects unstructured customer feedback with product data using TF-IDF/BERT, Spark, and AWS (SageMaker/S3), orchestrated with Airflow and monitored for drift. Also has hands-on experience with entity resolution at scale and improving search relevance via BERT embeddings, FAISS vector search, and domain fine-tuning validated with precision@k and A/B testing.”
Mid-level AI/ML Engineer specializing in recommender systems, fraud detection, and LLMs
Mid-level AI/ML Engineer specializing in NLP/LLMs and production ML systems
Angel Contreras
Senior Data Scientist specializing in machine learning, NLP, and MLOps
“ML/NLP engineer with experience building production-grade legal-tech and data platforms, including a GPT-4/LangChain contract review system using ElasticSearch embeddings (RAG) deployed on AWS EKS. Strong in entity resolution and scalable batch/streaming pipelines (Kafka/Spark), with measurable impact (70%+ reduction in contract review time) and a focus on monitoring and CI/CD for reliable delivery.”
Mid-level Machine Learning Engineer specializing in MLOps, RAG, and real-time personalization
Mid-level AI/ML Engineer specializing in GenAI agents and production ML systems
Mid-level AI/ML Engineer specializing in GenAI, LLMs, and RAG pipelines
Senior Machine Learning Engineer specializing in GenAI, NLP, and MLOps
Senior Data/GenAI Engineer specializing in cloud-native ML, RAG, and real-time data platforms
Mid-level AI/ML Engineer specializing in LLMs, agentic systems, and MLOps
Jisvitha Athaluri
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.”
Yeshwanth Pulapa
Mid-level AI/ML Engineer specializing in Databricks, MLOps, and real-time fraud detection
“ML/LLM engineer building production, real-time fraud detection for financial transactions using a two-tier architecture (fast ML + GPT) to deliver both low-latency decisions and analyst-friendly risk explanations. Experienced orchestrating end-to-end retraining, drift monitoring, and automated model promotion with Databricks Jobs/Workflows and MLflow, and partnering closely with fraud analysts to tune alerts, thresholds, and dashboards.”
Harish Gaddam
Mid-level AI/ML Engineer specializing in LLM agents and RAG systems
“LLM/agentic systems builder at Verizon who deployed a LangGraph-orchestrated multi-agent ticket-automation platform with RAG (FAISS) to replace brittle rule-based bots. Improved routing correctness by ~30–40%, hit ~300ms latency targets via model routing, and reduced ops workload by ~60% through tight iteration with non-technical stakeholders and strong testing/observability practices.”
Mid-level Machine Learning Engineer specializing in LLMs and ML at scale
Senior Data Scientist specializing in AI agents and LLM production systems
Mid-level AI/ML Engineer specializing in Databricks, MLOps, and real-time fraud detection
Mid-level AI/ML Engineer specializing in LLM fine-tuning, NLP, and MLOps
Abhishikth Meesala
Mid-level AI/ML Engineer specializing in NLP, Generative AI, and fraud detection
“At PwC, built and productionized an agentic RAG enterprise search assistant over 6M internal documents (8M embeddings), deployed across AWS and GCP. Drove major retrieval gains (72%→92% precision via BM25+dense hybrid with RRF and cross-encoder re-ranking), reduced hallucinations 30%, achieved <2s latency at 50–60K queries/month, and cut support tickets 30%—boosting adoption to 2,500 users by adding source-cited answers.”