Vetted XGBoost Professionals

“AI/LLM engineer with production experience at NVIDIA and Microsoft, including building a RAG-based enterprise knowledge assistant that improved accuracy by 42% and scaled to thousands of queries. Deep in inference optimization (TensorRT-LLM, Triton, quantization, speculative decoding) and MLOps/observability (Prometheus/Grafana, MLflow, LangSmith), plus orchestration with Kubeflow/Airflow across multi-cloud.”

“Amazon internship experience building production GenAI analytics for the returns organization: a multi-agent LLM+RAG system that let analysts query multiple heterogeneous data sources in natural language without hand-written SQL. Also built and operationalized four Apache Airflow DAGs for large-scale ETL, emphasizing observability and freshness-aware metadata to keep outputs accurate and up to date.”

“Backend/retrieval-focused engineer with production experience at Perplexity building a large-scale real-time Q&A system using retrieval-augmented generation, emphasizing low-latency, high-quality answers through ranking, context optimization, and caching. Also has orchestration experience from both product-facing LLM pipelines and large-scale infrastructure workflows at Meta, and has partnered with non-technical stakeholders to align AI trade-offs with business goals.”

“Machine learning/NLP practitioner at J.P. Morgan who led development of a production RAG system and an entity resolution pipeline for complex financial data. Deep hands-on experience with embeddings (Sentence-BERT), vector search (FAISS/pgvector), LLM fine-tuning (LoRA/PEFT), and rigorous evaluation (human-in-the-loop + A/B testing) backed by strong MLOps on AWS (Docker/Kubernetes, MLflow, Prometheus/Datadog).”

“Built and productionized an AWS-hosted, Kubernetes-orchestrated RAG assistant that enables natural-language Q&A over internal document repositories with grounded answers and citations. Demonstrates strong applied LLM engineering: hallucination mitigation, hybrid retrieval + re-ranking, and rigorous evaluation via benchmarks and A/B testing, plus real-world scaling of compute-heavy inference with dynamic batching and monitoring.”

“Built and deployed an LLM-powered platform that turns models into scalable REST/gRPC APIs, focusing on keeping GPU-backed inference fast and stable during traffic spikes. Experienced with AWS orchestration (EKS/ECS/Step Functions), safe model rollouts, and production-grade monitoring/testing for reliable AI agents and workflows.”

“Built and deployed a production clinical support LLM assistant at Mayo Clinic using a LangChain-orchestrated RAG architecture (Llama 2/PaLM) over de-identified clinical records, integrating BigQuery with Pinecone for semantic retrieval. Focused on healthcare-critical reliability by reducing hallucinations through grounding, implementing HIPAA-aligned privacy controls (Cloud DLP, VPC Service Controls), and running structured evaluations with clinician feedback.”

“AWS AI Lab engineer who deployed a production Chain-of-Thought analytical agent for tabular reasoning, emphasizing grounded tool-constrained workflows with schema-validated intermediate outputs. Built robust evaluation/logging with step-level observability to catch regressions across model versions, and has experience scaling distributed LLM training via Slurm + DeepSpeed/FSDP with checkpointing and failure recovery.”

“Built and deployed an AI receptionist product for field-service businesses (HVAC/electrician), including real-time Jobber scheduling integrations and Twilio-based calling. Combines hands-on customer/operator shadowing with strong production engineering (queueing to handle API limits, rigorous testing/mocking, mirrored prod environment) and cross-layer troubleshooting, driving user adoption through review/override workflows.”