Vetted Natural Language Processing Professionals

“Robotics software engineer from Ati Motors who brought a Linear MPC approach (based on Kuhne et al.) into production, rebuilding parts of the planning stack to eliminate oscillations and safely double AMR speed from 0.8 m/s to 1.6 m/s. Also delivered an end-to-end point-cloud detection pipeline (PointPillars) including synthetic data generation in Isaac Sim and TensorRT deployment for real-time human/trolley detection, with a strong focus on production reliability via iterative hardening and nightly SIL.”

“Built and deployed a production LLM-powered risk assistant for KPMG and Freddie Mac that lets analysts query a confidential Neo4j risk graph in natural language (no Cypher), turning multi-day analysis into minutes with traceable, cited answers. Implemented rigorous guardrails, deterministic verification, RBAC/security controls, and a full eval/observability stack, cutting query error rate by ~50% and iterating through weekly UAT with non-technical risk analysts.”

“CMU IoT coursework project builder who implemented an end-to-end TinyML gesture recognition system on a Particle Photon + ADXL345, streaming data via MQTT/Node-RED to a real-time Node.js frontend and deploying a quantized logistic regression model on-device. Also explored multi-drone coordination, implementing leader-follower offset control and a pivot/arc turning strategy to avoid collisions, and brings practical Docker/Kubernetes plus CI/CD workflow experience from internships.”

“AWS engineer who productionized an internal ML-driven data pipeline from a notebook prototype into a scalable, observable Python service (schema validation, deduplication, idempotency, safe retries, versioned transforms, CloudWatch alarms), reducing manual effort and improving data accuracy/trust. Experienced diagnosing workflow issues in real time (e.g., upstream schema changes) and partnering with account managers/support to unblock adoption of seller-facing Marketplace features by demonstrating reliability with concrete metrics.”

“Built a production LLM-driven archival assistant that turns large, low-quality scanned handwritten files (120+ pages) into structured datasets, overcoming context-window and hierarchy challenges with a two-phase LLM + rules pipeline and reaching 98.1% accuracy (Gemini-2.5 Flash). Also orchestrated a large human-in-the-loop effort with 78 archivists, producing 2,400 high-quality annotations in 4 days via detailed rubrics and support.”

“Senior engineering leader in the Bay Area with experience spanning VMware, Hortonworks/Cloudera, Barracuda, and Palo Alto Networks, including leading open-source work (Apache Knox) and architecting large-scale security platforms. Has driven disaster recovery and cloud security products, designed Python microservices for Microsoft 365 security, and scaled teams (3x) while formalizing enterprise readiness practices with automated documentation using Notebook LLM.”

“Backend/data engineer with Optum healthcare claims domain experience building high-reliability Python microservices (FastAPI/Kafka/Postgres) and AWS data platforms (EKS, Glue, Redshift). Demonstrated strong production ownership: fixed duplicate Kafka processing via transactional outbox/idempotency, scaled to millions of daily events, and delivered major SQL performance gains (40+ min to <5 min, ~60% CPU reduction). Seeking remote-only work; targets $130k base.”

“Built production LLM systems focused on reliability and safety, including a plain-English deployment tool that generates validated plans and provisions to Kubernetes while preventing unsafe actions via schema enforcement and plan/execute separation. Also created multi-LLM workflows (LangGraph) and stakeholder-friendly demos at Bosch, including a PyQt/FastAPI/CUDA app comparing SAM2 vs SAMWISE for on-device object detection with intuitive UX for business users.”

“Built and shipped a production LLM-driven multi-agent platform (ciATHENA) at CustomerInsights.AI to automate analytics/ML/compliance workflows in healthcare and life sciences. Implemented LangGraph/LangChain orchestration with strong backend-style rigor (schemas, Pydantic validation, retries, auditability) and optimized latency/cost while keeping the system usable for non-technical users via guided natural-language interactions and structured/visual outputs.”

“Backend/data engineer who has owned production Python APIs and high-throughput async workflows on AWS (FastAPI, Docker, ECS/EKS/Lambda) with mature reliability practices like idempotency, bounded retries, circuit breakers, and strong observability. Also built AWS Glue ETL into an S3/Redshift lakehouse and modernized legacy batch systems via parallel-run parity testing and feature-flagged migrations, including a SQL tuning win cutting a multi-minute query to under 10 seconds.”

“Built and deployed an agentic, multimodal LLM system that automates privacy redaction pipelines (audio/video/tabular) using LangChain orchestration and a closed-loop self-correction design. Personally implemented and performance-optimized core CV tooling (face blurring with tracking/Kalman filter) achieving >100 FPS on CPU, and validated reliability with golden-dataset benchmarking across 100+ privacy intents and measurable redaction metrics.”

“Machine learning software engineer intern experience at Amazon, where they built a production testing framework to inject frames/videos onto devices to measure embedded CV model inference and ensure broad model compatibility via automatic NNA metadata handling. Also built side projects spanning LLM/RAG orchestration (LangChain/LangGraph with reranking and citations) and applied CV/healthcare work (nail disease detection, medical retrieval chatbot).”

“Built a production RAG-based "knowledge copilot" for support/ops using LangChain/LangGraph, implementing the full pipeline (ingestion, chunking, embeddings, vector DB retrieval/rerank, guarded generation with citations) and operating it as monitored microservices with CI/CD. Also designed an event-driven, streaming backend for real-time inventory ordering predictions that reduced stockouts by 25%, and has hands-on incident response experience stabilizing LLM API latency/5xx spikes using Datadog/APM and resilience patterns.”