Vetted Data Preprocessing Professionals

“Built and productionized a real-time enterprise RAG pipeline to improve factual accuracy and reduce LLM hallucinations by grounding responses in constantly changing internal knowledge bases (policies, manuals, FAQs). Experienced in orchestrating end-to-end ML workflows (Airflow/Kubernetes), handling messy multi-format data with schema enforcement (Pydantic/Hydra), and maintaining freshness via streaming incremental embeddings plus batch refresh. Also delivers applied ML solutions with non-technical teams (marketing/CRM) for segmentation and personalized engagement.”

“Embedded/robotics software engineer with Hyundai Motors experience who owned an AI-driven perception validation pipeline using a Transformer-based approach to generate stable synthetic in-cabin audio for autonomy/ASR testing, cutting downstream testing time by 50%+. Has hands-on ROS integration (IMU sensor streaming, inference, control nodes), MQTT-based distributed messaging, and cloud/container deployment experience (Docker, Node/Express, AWS, CI/CD).”

“LLM/agentic-systems builder who has shipped production tools for investment research and procurement insights, including a company screener that processes thousands of conference-listed companies using FireCrawl + Google Search + Gemini. Demonstrates strong orchestration expertise (LangGraph multi-agent graphs), performance optimization (async/batching to sub-30s), and pragmatic reliability/evaluation practices with stakeholder-friendly UX (real-time cost tracking and model/parameter toggles).”

“Built and shipped a production LLM-powered medical scribe that generates structured clinical visit summaries using RAG, strict JSON schemas, and post-generation validation to reduce hallucinations. Experienced in making LLM workflows deterministic and observable (structured logging/metrics/tracing) and in evaluation-driven iteration with metrics like schema pass rate and edit rate; collaborated closely with clinicians and policy stakeholders at Scale AI to drive adoption.”

“Built and shipped a real-time AI phone agent for small businesses that handles bookings/FAQs/messages using streaming ASR, an LLM with tool-calling, and TTS; deployed to production for multiple paying customers. Demonstrates strong applied LLM reliability practices (tool-first grounding, retrieval, hard-negative testing, and production monitoring) and experience orchestrating multi-step AI workflows with Airflow, Prefect, and AWS Step Functions.”

“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.”