Vetted XGBoost Professionals

“AI/NLP-focused practitioner who built a zero-/few-shot LLM event extraction system on the long-tail Maven dataset, combining prompt-structured outputs with LoRA/QLoRA fine-tuning and rigorous F1 evaluation. Also implemented entity resolution/data cleaning pipelines and embedding-based semantic search using Sentence-BERT + FAISS, and has healthcare experience delivering a multilingual speech/translation mobile prototype using HIPAA-compliant Azure Cognitive Services.”

“AI/ML practitioner in healthcare/insurance (Blue Cross Blue Shield) who built and deployed a production NLP system to classify patient risk from unstructured clinical notes. Experienced in end-to-end pipeline orchestration (Airflow, AWS Step Functions/Lambda/SageMaker) and real-time optimization (BERT to DistilBERT on AWS GPUs), with strong clinician collaboration to drive adoption.”

“Founding/early engineer experience across Asante and a Series A startup (Adgency), shifting from data science/ML into owning production full-stack systems end-to-end. Built core product flows (registration, business profiles, map service), AWS-deployed gRPC microservices with CI/CD, and operated low-latency agent/video ad generation workflows with retries/fallbacks and PostHog-based observability.”

“Built and productionized an LLM-powered financial intelligence and forecasting platform at Northern Trust using a RAG architecture (LangChain + Hugging Face + FAISS) with end-to-end MLOps (Docker/Kubernetes, Airflow, MLflow). Emphasized regulatory-grade explainability (SHAP/Power BI) and hallucination control (retrieval-only grounding), achieving ~30% forecasting accuracy improvement and ~65% reduction in analyst research time, with sub-second inference and 95% uptime on EKS/AKS.”

“Built a production LLM-RAG system at McKesson to let internal healthcare operations teams query large volumes of unstructured operational documents via natural language with source-backed answers, designed with HIPAA/FHIR compliance in mind. Demonstrated strong production engineering across hallucination mitigation, retrieval quality tuning, and latency/scalability optimization, using LangChain/LangGraph and Airflow plus rigorous evaluation/monitoring practices.”

“Healthcare AI/ML engineer with Epic experience who built and deployed a HIPAA-compliant GPT-4 RAG clinical assistant over large medical document sets, emphasizing privacy controls and low-latency performance. Also automated end-to-end retraining and deployment of patient risk models using orchestration/CI-CD (Jenkins, SageMaker, MLflow), cutting deployment time from hours to minutes while improving reliability.”

“Software engineer with experience at Wipro Technologies and Wells Fargo building React-based SPAs, reusable component libraries, and developer documentation. Demonstrated strong performance engineering (React.memo, list virtualization, code splitting) with reported >50% rendering-time improvement, plus hands-on production support by diagnosing API outages via monitoring/logs and implementing traffic/server fixes. Comfortable leading workstreams in fast-changing environments using Kanban and tight stakeholder feedback loops.”

“ML/MLOps practitioner from Discover Financial who built and deployed a real-time AI fraud detection platform (LSTM + VAE) on AWS SageMaker with Docker/FastAPI and Jenkins-driven CI/CD. Demonstrated measurable impact (30% accuracy lift, 25% fewer false alerts) and deep expertise in class-imbalance mitigation, drift monitoring, and orchestration (Airflow/Kubeflow), plus strong stakeholder adoption via Power BI dashboards for fraud/compliance teams.”

“Software/ML engineer who built a production automated job-finding and cold-email personalization system for Fortune 500 outreach, using JobSpy for dynamic scraping, LangChain orchestration, and LLM+vector DB semantic search with grounding/relevance metrics and guardrails. Also delivered a predictive investment analytics platform for financial advisors, communicating results via Tableau dashboards and portfolio KPIs like Sharpe ratio and drawdowns.”

“Machine learning/NLP engineer with deep Azure stack experience (Data Factory, Databricks/Spark, Delta Lake, Azure OpenAI, Azure AI Search) who built end-to-end production systems for semantic clustering, entity resolution, and hybrid search. Demonstrated measurable gains from embedding fine-tuning (~15% retrieval precision, ~10–12% nDCG@10) and designed scalable, quality-checked pipelines with MLOps best practices.”

“Built an Arduino-based obstacle-avoiding robot using sonar/laser sensors and improved performance from 0.60 to 0.87 accuracy through sensor-fusion thresholding and iterative tuning. In an internship, optimized a legal-document NLP pipeline by switching to a distilled/quantized transformer and offloading inference to a GPU-backed Flask service, cutting inference time by 40%+ without added infrastructure spend.”

“Built and shipped a production RAG assistant using GPT-4, LangChain, and Pinecone/FAISS to search 50K+ institutional documents, with a strong focus on groundedness and hallucination reduction through retrieval optimization and re-ranking. Pairs this with a metrics-driven evaluation/monitoring approach (BLEU/ROUGE, manual sampling, logging) and workflow automation via Airflow, and has experience translating stakeholder needs into iterative AI prototypes.”

“Robotics software engineer focused on hard real-time autonomy for legged robots, building a quadruped navigation stack that combines vision SLAM with MPC and maintains a deterministic 500Hz control loop. Deep performance optimization experience across CUDA (sub-2ms perception latency), ROS 2/DDS real-time tuning, and motion planning (cut 500ms spikes to sub-5ms). Also designed distributed ROS 2 + Zenoh communications between quadrupeds and aerial drones and validated robustness under lossy wireless conditions.”

“Backend engineer who built an AI-driven "Smart Feedback Analyzer" API (Flask → FastAPI) that processes user feedback with NLP (Hugging Face + OpenAI) and returns structured insights. Demonstrates strong production-minded architecture: stateless services, Cloud Run + Docker deployment, Redis/Celery background processing, and Postgres/SQLAlchemy performance tuning (EXPLAIN ANALYZE, indexing, N+1 fixes), plus multi-tenant data isolation via JWT/API-key derived tenant IDs.”

“AI Automation Engineer at BalancedTrust who single-handedly shipped production LLM features for FinTech compliance: a policy gap-analysis pipeline (SOC 2/GDPR) and a RAG-based regulatory chatbot. Deeply focused on reliability in high-stakes legal/compliance settings, with strong production engineering (edge functions, parallelized batching to cut latency, structured JSON outputs, guardrails, and monitoring) and close collaboration with non-technical compliance experts.”

“AI/ML engineer with experience at Northern Trust and Persistent Systems building production LLM + RAG systems for regulated financial use cases, including liquidity forecasting, anomaly detection, and credit scoring. Emphasizes compliance-first design with explainability (SHAP), traceability (MLflow), and hallucination controls (FAISS + citation-grounded prompting), and has delivered drift-triggered retraining pipelines using Airflow and Kubernetes while translating model outputs into business-ready marketing segments.”

“Built and deployed a production LLM/RAG clinical document understanding and summarization system for healthcare, focused on reducing manual review time while meeting strict accuracy, latency, and compliance needs. Demonstrates strong MLOps/orchestration depth (Airflow, Kubernetes, Azure ML Pipelines) and a rigorous approach to hallucination mitigation through layered, source-grounded safeguards and stakeholder-driven requirements with physicians/compliance teams.”

“Built and deployed JobMatcher AI, an LLM-driven workflow automation product for job seekers that extracts requirements from job descriptions, matches to user skills, and generates tailored outreach. Demonstrated strong production engineering by cutting per-run cost ~70%, improving reliability with retries/backoff/fallbacks, and reducing hallucinations via schema validation and templating; also orchestrated the system with LangGraph plus Docker Compose across API, vector DB, and workers.”

“Built and deployed an enterprise RAG application at Centene to help clinical teams retrieve insights from large internal policy document sets, cutting manual research by 30–40%. Implemented custom domain-adapted embeddings (SageMaker + BERT transfer learning) and hybrid retrieval (BM25 + Pinecone) to drive a 22% relevance lift, and ran the system in production on AWS EKS with CI/CD, MLflow, and Prometheus monitoring (99% uptime, ~40% latency reduction).”

“AI/ML engineer (Merkle) with hands-on experience deploying RAG-based LLM applications and real-time recommendation engines into production. Strong in cloud/on-prem architectures, GPU autoscaling, caching, and network optimization—delivered measurable latency reductions (40–70%) and improved retrieval relevance by systematically benchmarking chunking/embedding configurations and validating pipelines via CI/CD.”

“Healthcare data/NLP practitioner with experience at UnitedHealthcare building production ML systems that connect unstructured call center transcripts and medical notes to structured claims data. Has delivered measurable impact (25% classification accuracy lift; ~30% relevance improvement) using classical NLP, embeddings (Sentence-BERT + FAISS), and AWS SageMaker deployments with robust validation and drift monitoring.”

“Data/ML practitioner with experience at Humana and Hexaware, focused on turning messy, semi-structured datasets into production-ready pipelines. Built an age-prediction model from book ratings using heavy feature engineering and multiple regression models, and has hands-on entity resolution (deterministic + fuzzy matching) plus embeddings/vector DB approaches for linking and search relevance.”

“Data professional with ~4 years of experience, most recently at AIG (insurance), building ML/NLP systems for fraud detection and policy automation using transformers, CNNs, and clustering/anomaly detection. Also developed a RAG-based knowledge retrieval system, iterating across embedding models and moving to production based on precision and latency SLAs, then containerizing and deploying with SageMaker and CI/CD.”

“Built and productionized an LLM-based financial document analysis system using a RAG pipeline, including robust ingestion/chunking/embedding workflows, vector DB retrieval, and an AWS-deployed FastAPI service containerized with Docker. Demonstrates strong applied expertise in improving retrieval quality and latency at scale, plus hands-on experience debugging agentic/LLM workflows with monitoring and trace-based analysis while supporting demos and customer-facing adoption.”