Vetted Neural Networks Professionals

“Robotics/IoT engineer who deployed a fog-enabled real-time monitoring system (edge Raspberry Pi + MQTT + cloud logging) and validated it via an IEEE-indexed publication. Strong in autonomous navigation with ROS/Gazebo, SLAM/localization, and cross-layer debugging using timing/transform-delay correlation. Extends Python computer vision pipelines (YOLO + OpenCV/Albumentations) for custom datasets and weather-specific conditions.”

“Embedded C++ engineer with Bosch automotive infotainment experience, owning real-time audio middleware modules with strict latency/memory constraints. Strong in profiling/optimizing deterministic behavior, debugging hardware-specific intermittent issues, and building automated test + CI pipelines; currently ramping up on ROS2 concepts (DDS, nodes/topics/services) to transition toward robotics.”

“Developed and productionized an AI-driven "indication finding" system for AbbVie to identify additional diseases a drug could target, working closely with clinical research teams on cohort inclusion/exclusion criteria and disease rollups. Leveraged an LLM to map clinical inputs to ICD codes and built configuration-driven ML pipelines (Cloudera ML, YAML, scheduled jobs) with structured testing and evaluation for reliability.”

“LLM/agent workflow engineer with healthcare experience (CVS/CBS Health) who built and deployed a production call-insights platform using Azure OpenAI + LangChain/LangGraph, including sentiment and compliance checks. Demonstrates deep HIPAA/PHI handling (tenant-contained processing, redaction, RBAC/encryption/audit logging) and production rigor (testing, eval sets, validation/retries, autoscaling) to scale to thousands of transcripts.”

“Built an LLM-powered learning assistant (EduQuizPro/EduCrest Pro) that uses RAG over URLs and PDFs to generate quizzes, notes, and explanations for students/professors. Emphasizes production robustness—implemented dependency fallbacks (FAISS/Sentence Transformers/Gradio), CLI-safe mode, and NumPy-based indexing—along with a custom orchestration layer to keep multi-step AI workflows reliable.”

“Built a production GenAI-powered analytics assistant to reduce reliance on data analysts by enabling natural-language Q&A over Databricks/Power BI dashboards, backed by vector search (Pinecone/Milvus) and a Neo4j knowledge graph, including multimodal support via OpenAI Vision. Demonstrates strong real-world LLM reliability engineering with strict RAG, LangGraph multi-step verification, and Guardrails/custom validators, plus broad orchestration and production monitoring experience (Airflow, ADF, Step Functions, Kubernetes, Prometheus/CloudWatch).”

“Master’s-level ML researcher with hands-on embodied/edge deployment experience: built a Google Glass motion-tracking system at Sandia using MobileNetV1 + LSTM trained in TensorFlow and deployed via TensorFlow Lite. Has reimplemented transformer-based research for a thesis and demonstrated strong judgment adapting quickly when upstream assumptions changed, and stays current through active reading groups and a JEPA collaboration.”

“Engineer with hands-on experience debugging and hardening a fixed-point implementation for an internal PoC, quickly diagnosing overflow/underflow issues that caused intermittent failures across thousands of runs and delivering a code fix. Comfortable presenting technical solutions with layered slide depth and doing follow-up deep dives for interested stakeholders, though has limited direct customer/sales partnership experience.”

“ML/NLP engineer currently working in NYC on a system that connects complex unstructured data sources to deliver personalized insights, using embeddings + vector DB retrieval and a RAG architecture (LangChain, Pinecone/OpenSearch). Strong focus on production constraints—especially low-latency retrieval—using FAISS/ANN, PCA, index partitioning, and Redis caching, plus PEFT fine-tuning (LoRA/QLoRA) and KPI/SLA-driven promotion to production.”

“Insurance-tech CTO and repeat founder with 10+ years in insurance startups; was employee #4/CTO at Polly (formerly DealerPolicy) and helped scale it from a PowerPoint to 250 employees while raising $180M+. Currently building and selling AgentCanvas.ai—an extensible AI accelerator platform for large insurance agencies—after coding the product end-to-end and now running demos/POCs with prospective buyers.”

“At Evoke Tech, built a production LLM "Testbench" to quickly compare LLMs/embedding models and RAG strategies (semantic, hybrid BM25, re-ranking, HyDE, query expansion) to select optimal architectures for different client needs. Also developed a multi-agent, multimodal (voice/text) RAG system for live catalog retrieval and safe product recommendations using LangGraph/LangChain with LangSmith monitoring, and regularly translated PM/UX goals into concrete agent behaviors via demos and flowcharts.”

“Enterprise platform engineer/product owner who led end-to-end delivery of customer-facing ServiceNow Service Catalog/workflow solutions, emphasizing reliability, security, and fast iteration. Built React/TypeScript portals with Node.js and Spring Boot backends, and improved microservices reliability at scale using Kafka, monitoring, and robust retry/timeout patterns.”

“Early-career (fresher) candidate who built and deployed a production AI medical document chatbot using a RAG architecture (LangChain + Hugging Face LLM + Pinecone) with a Flask backend on AWS EC2 via Docker. Has experience troubleshooting real deployment constraints (model dependencies, disk space, container stability) and setting up continuous-style evaluation with fixed query test sets tracking relevance, latency, and error rate.”

“Internship experience building a production RAG+LLM pipeline to map messy card transaction descriptions to merchant brands, including a custom modified-ROUGE evaluation approach for weak/variant ground truth. Improved scalability and cost by moving from a managed LLM endpoint (e.g., Bedrock) to self-hosted vLLM, and orchestrated massive embedding backfills (5,000+ files, 10B+ rows) using an Airflow-triggered SQS + ECS worker architecture with robust retry/DLQ handling.”

“Machine learning/NLP engineer who built a production-oriented retrieval-based AI system at Morgan Stanley for healthcare use cases, combining RAG over unstructured patient records with deep-learning medical image segmentation (U-Net/Mask R-CNN). Strong in end-to-end pipelines and MLOps (Spark/MongoDB, AWS SageMaker, CI/CD, monitoring, automated retraining) and in entity resolution/data quality validation for noisy clinical data.”

“GenAI/ML engineer with production experience building multilingual LLM systems (English/Spanish) and RAG-based clinical documentation summarization at Walgreens, combining prompt engineering, structured output validation, and rigorous evaluation (ROUGE + pharmacist review). Also orchestrated end-to-end ML pipelines for demand forecasting using Apache Airflow, PySpark, and MLflow with scheduled retraining and production monitoring.”

“Built and shipped an end-to-end podcast recommendation system exposed via a Flask API and React UI, explicitly balancing relevance, diversity (MMR), and safety constraints while meeting ~200ms latency targets. Also implemented a production-style RAG/information-extraction pipeline using web retrieval, spaCy NER, and fine-tuned SpanBERT with guardrails and evaluation loops (precision/recall/F1) to tune confidence thresholds and improve reliability.”

“Robotics engineer/team lead who built an autonomous lunar rover for the NASA Lunabotics challenge, owning electrical systems, comms (UART/WiFi), and autonomy using RealSense + custom YOLO on a Jetson Orin Nano. Also has extensive ROS 2 experience, including creating a ROS2+Gazebo environment with ros2_control custom controllers for a large-scale additive manufacturing printer and research/thesis work in multi-robot coordination and robustness.”

“Robotics software engineer focused on autonomous drones and mobile robotics: implemented a sliding mode inner-loop controller and a RealSense T265 VIO state-estimation pipeline integrated into ArduPilot EKF3 for GPS-denied indoor flight. Strong simulation-to-deployment experience (Gazebo/MAVROS to firmware), ROS2 networking/debugging, and hands-on validation through multi-sensor trials and log analysis.”