Vetted Machine Learning Engineers in Indiana

“Built and deployed a production LLM-powered clinical insights/summarization assistant for healthcare teams, including a Spark+Airflow pipeline, fine-tuned transformer models, and a FastAPI Docker service on AWS. Demonstrates strong MLOps/LLMOps depth (Airflow on Kubernetes, custom AWS operators/IAM, MLflow, CloudWatch) and practical reliability work like hallucination mitigation, confidence scoring, and retrieval-backed evaluation with shadow deployments.”

“Built and deployed a production LLM-powered RAG assistant for healthcare teams (care managers/support) to answer questions from clinical and policy documentation, emphasizing trustworthiness via improved retrieval, reranking, and strict grounding prompts to reduce hallucinations. Also has hands-on orchestration experience with Apache Airflow for end-to-end ETL/ML workflows and applies rigorous testing/metrics (hallucination rate, tool-call accuracy, latency, cost) to ensure reliable AI agent behavior.”

“ML/LLM engineer with Meta experience building production AI systems for near real-time user-report classification and summarization under strict latency (<250ms), safety, cost, and privacy constraints. Has hands-on MLOps/orchestration experience (Airflow, Spark, MLflow, Kubernetes, Docker, GitHub Actions) plus observability (Prometheus/Grafana) and applies rigorous evaluation, staged rollouts, and A/B testing to keep agent workflows reliable in production.”