Pre-screened and vetted in Texas.
Mid-level AI Engineer specializing in Generative AI, LLMs, and RAG systems
Mid-level AI Engineer specializing in Ambient AI and full-stack applications
Mid-Level Full-Stack Software Engineer specializing in web platforms and AI research
Mid-level AI/ML Engineer specializing in real-time fraud detection and healthcare computer vision
Mid-level AI/ML Engineer specializing in recommender systems, MLOps, and Generative AI
Mid-level Machine Learning Engineer specializing in LLMs and ML at scale
Mid-level AI/ML Engineer specializing in Databricks, MLOps, and real-time fraud detection
Senior Staff Data Scientist specializing in AI/ML and LLM-powered analytics
Mid-level AI Engineer specializing in LLM agents, RAG, and knowledge graphs
Senior AI Engineer specializing in NLP, LLMs, and healthcare platforms
Mid-level AI/ML Engineer specializing in LLM fine-tuning, NLP, and MLOps
Junior Full-Stack Software Engineer specializing in ML, cloud infrastructure, and LLM agents
Mid-level AI/ML Data Engineer specializing in analytics, ML pipelines, and LLM applications
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.”
Mid-level Machine Learning Engineer specializing in industrial deep learning and predictive control
“AI engineer building and deploying deep-learning-based optimization/control systems for petrochemical plants, with a focus on maintaining operational stability under real-world constraints. Core contributor to model and inference design; introduced a stability-focused non-linear objective and sped up second-layer optimization via on-the-fly first-order approximations. Experienced using Kubernetes for end-to-end testing and effective in translating customer expectations into measurable evaluation plots for non-technical stakeholders.”
Mid-level AI/ML Engineer specializing in healthcare and financial analytics
“ML engineer with production experience across healthcare and fraud domains, including end-to-end ownership of a telecare patient deterioration system at Oracle Health and a GPT-4/RAG fraud reporting solution at Cognizant. Stands out for combining scalable data/ML infrastructure, clinical NLP, and GenAI delivery with measurable gains in model quality and workflow efficiency.”
Mid-level AI/ML Engineer specializing in Generative AI and RAG systems
Mid-Level AI/ML Software Engineer specializing in agentic LLM systems
“Built and deployed a production LLM-powered multi-agent compliance copilot (life sciences/finance) using LangChain/LangGraph + RAG over vector databases, delivered via async FastAPI on Kubernetes. Emphasizes audit-ready, deterministic outputs with schema constraints and citations, plus rigorous evaluation/monitoring; reports 60%+ reduction in manual research time and successful production adoption.”
Junior Machine Learning Engineer specializing in MLOps and LLM/RAG systems
“LLM/agentic workflow builder focused on productionizing document-processing systems. Redesigned pipelines with LangGraph + RAG, schema-aware validation, and eval/monitoring loops; known for fast incident diagnosis (restored accuracy from ~70% to >95% same day). Partners closely with sales and stakeholders to deliver tailored demos and drive adoption (reported +40%).”
“LLM/agent workflow engineer with healthcare experience (CVS/CBS Health) who built and deployed a production call-insights platform using Azure OpenAI + LangChain/LangGraph, including sentiment and compliance checks. Demonstrates deep HIPAA/PHI handling (tenant-contained processing, redaction, RBAC/encryption/audit logging) and production rigor (testing, eval sets, validation/retries, autoscaling) to scale to thousands of transcripts.”