Vetted R Professionals

“Deloitte consultant who led cross-functional teams delivering a Snowflake/AWS data ingestion, warehousing, and analytics platform, with a strong track record of executive alignment and risk mitigation. Built reusable business-development accelerators (including an end-to-end Appian app and a Java integration-config tool) credited with helping secure $75M+ in contracts, and has high-confidentiality experience consulting for DoD and FDA.”

“Built and shipped a production agentic LLM tool that helps internal teams update technical product whitepapers using plain-language edit requests, with strong guardrails (citations, verification, refusal/clarify flows) to reduce hallucinations and maintain compliance. Experienced taking LLM workflows from rapid LangChain prototypes to more predictable, debuggable LangGraph agent graphs, and orchestrating end-to-end ingestion/embedding/indexing/eval/deploy pipelines with Kubeflow.”

“Backend/data engineer focused on production ML/LLM systems: built a real-time FastAPI inference API on Kubernetes with strong reliability patterns (timeouts, idempotent retries, centralized error handling). Delivered AWS platforms using EKS + Lambda with GitHub Actions/Helm CI/CD and built Glue-based ETL from S3/Kafka into Snowflake with schema evolution and data-quality controls; also modernized legacy analytics/recommendation workflows into Python services with safe, feature-flagged cutovers.”

“Product and consumer growth professional from a B2C app background, focused on improving retention/activation and driving revenue through customer/product data. Experienced in maintaining BI layers and dashboards and running KPI-driven analysis across returns/refunds operations (NPS/CSAT, TAT, repeat complaints). Familiar with core F2P monetization mechanics and how to evaluate IAP offers via A/B testing; has shipped/managed products on mobile and web.”

“Built and deployed a production LLM-powered clinical support assistant at BJC HealthCare (RAG + transformer) to answer patient questions, summarize clinical notes, and support appointment workflows. Implemented PHI-safe data pipelines (Spark/Hadoop/Kafka) with automated scrubbing, dataset versioning, and audit logs, and runs the system on Docker/Kubernetes with Pinecone vector search while partnering closely with clinical operations staff.”

“Robotics software engineer with hands-on ROS2 experience building an audio conversion node and integrating Whisper LiveKit for streaming speech-to-text in a simulated hostile (outer space) robot environment. Also worked on a 2023 LiDAR + ML vision obstacle-detection project for a hospital-nurse-assistant robot, and has strong large-scale CI/CD deployment experience from AWS (2022–2024) across alpha/pre-prod/prod stages.”

“Robotics software engineer who built and tuned a contact-implicit MPC controller for a full planar pushing manipulation pipeline (“Push Anything”), including a key fix for complementarity violations that eliminated “ghost pushes” and cut time-to-goal from 40s to 25s. Hands-on with ROS/MoveIt on real robot pick-and-place, improving hardware grasp reliability through TF/frame debugging, and uses Drake/URDF for simulation, contact detection, and MPC development.”

“AI/LLM engineer who built and productionized an internal RAG-based knowledge system that ingests diverse sources (PDFs, Markdown, Slack), scaled retrieval with distributed FAISS and parallel ingestion, and reduced hallucinations via re-ranking, grounding prompts, and post-generation validation. Also has hands-on orchestration experience with Airflow and Kubernetes for reliable ETL/model pipelines, monitoring, and staged rollouts; reports ~15% accuracy improvement and adoption as the primary internal knowledge tool.”

“Engineering leader with experience building point-of-care diagnostics platforms (IoT-connected PCR device delivering results in <15 minutes) and scaling multidisciplinary teams (55+). Has led major data/IoT architecture decisions (multi-cluster Kubernetes with secure routing; Kafka + Gobblin over MQTT) and runs execution with Agile roadmaps tightly aligned to GTM and senior leadership.”

“Embodied AI/robotics ML engineer with hands-on experience deploying POMDP-based reinforcement learning controllers on real mobile robots and vehicle fleets. Strong in sim-to-real robustness (domain randomization) and production rollout practices (HIL, shadow-mode, canaries, safety instrumentation), and has published related work (mentions a NeurIPS paper).”

“Amazon Prime Video backend engineer who built and operated high-traffic Python/FastAPI services and AWS-native data/batch systems. Demonstrates strong production reliability and incident ownership (CloudWatch/X-Ray), plus measurable performance wins (8s to <200ms query latency, ~40% CPU reduction) and cost-focused architectures (Lambda + ECS/Fargate with Fargate Spot).”

“Data science/NLP practitioner with experience at NVIDIA and Microsoft building production-grade NLP and data-linking systems. Has delivered high-performing pipelines (e.g., F1 0.92) and large-scale entity resolution (F1 0.89), plus semantic search using embeddings and Pinecone with ~30–40% relevance gains, backed by rigorous validation (A/B tests, ROUGE, MRR) and strong MLOps/workflow tooling (Airflow, Databricks, FastAPI, MLflow, Prometheus/ELK).”