Vetted Convolutional Neural Network (CNN) Professionals

“Full-stack engineer who builds and scales customer-facing and internal AI products end-to-end (React/TypeScript/FastAPI/MongoDB) with strong product instrumentation and rapid MVP iteration. Built an AI-powered code review assistant adopted across teams and integrated into CI/CD, reducing manual review time by 30%+, and has hands-on experience with LLM retrieval/reasoning systems (LangChain + FAISS) and microservices scaling using RabbitMQ, Docker, and AWS.”

“Robotics software lead at Icor who grew from intern to owning the end-to-end software lifecycle for a mobile manipulator platform deployed to 300+ customers globally. Deep hands-on ROS2/MoveIt2 and navigation-stack integration (URDF/TF, sensors, behavior engine) plus production infrastructure (CI/CD, OTA, field OS upgrades) and real-world performance tuning for motion planning in EOD multi-robot environments.”

“AI Engineer at DXC Technology who has shipped production LLM/NLP systems on AWS (SageMaker, FastAPI) and optimized them for real-time latency and unpredictable traffic using quantization, batching, and autoscaling. Strong MLOps and monitoring discipline (MLflow, CloudWatch, SageMaker Model Monitor) and proven business impact—delivered models with 92% predictive accuracy and cut enterprise decision-making time by 30% through close collaboration with product managers.”

“Built and deployed production LLM systems for summarizing sensitive legal and financial documents, emphasizing GDPR-aligned privacy controls and scalable hybrid cloud architecture. Experienced with Kubernetes/Airflow orchestration and rigorous testing/monitoring practices, and has delivered measurable business impact (18% conversion lift) by translating AI outputs for non-technical marketing stakeholders.”

“Built a production multi-agent cybersecurity defense simulator orchestrated with CrewAI, combining Red/Blue team LLM agents, a RAG runbook retriever, and an RL remediation agent trained via state-space simplification and reward shaping for rapid incident response. Also partnered with quant analysts and fund managers to deliver an automated trading and portfolio management system using statistical methods plus CNN/LSTM models, reporting up to 15% weekly ROI.”

“Robotics software engineer focused on ROS2 autonomy stacks, with hands-on work spanning semantic 3D SLAM, sensor fusion, and controller customization. Built an indoor GPS-denied semantic SLAM system (>95% accuracy) and extended Nav2’s MPPI controller with a custom C++ critic to keep an agricultural rover centered in crop rows, boosting CO2 laser weeding effectiveness by 40%. Strong in simulation-to-real workflows (Isaac Sim, Gazebo Ignition) and deployment automation (Docker on Jetson Orin NX, GitHub Actions CI/CD).”

“Deployment engineer at a robotics startup who owned end-to-end field deployments in greenhouse environments, including integrating humanoid robots (XArm 6), tuning perception stacks for real-world lighting shifts, and coordinating rapid fixes with hardware/software teams. Experienced debugging complex robotics integrations (LiDAR + NVIDIA Jetson + ROS2 + networking) and hardening solutions by automating configuration at boot, while also working directly with customers and training operators for ongoing support.”

“Backend/AI engineer who has shipped production LLM systems end-to-end, including an AI request-routing service (FastAPI + BART MNLI + OpenAI/Gemini) that improved accuracy ~25% after launch via eval-driven prompt/category iteration. Also built an enterprise document intelligence/RAG platform on Azure (Blob/SharePoint/Teams ingestion, OCR/NLP chunking, embeddings in Azure Cognitive Search) with PII guardrails (Presidio), confidence gating, and scalable event-driven pipelines handling millions of documents.”

“Built and productionized an agentic RAG assistant using Ollama + LangChain + MCP + ChromaDB to speed up and standardize access to operational knowledge from tickets and runbooks. Focused on real-world reliability: mitigated timeouts/latency with retries and concurrency limits, improved retrieval via chunking/embedding iteration, and reduced hallucinations through citation-grounding and confidence-based abstention. Also partnered with non-technical ops staff to deliver anomaly detection/monitoring by translating operational needs into model signals, thresholds, and alerting logic.”

“Cross-functional operator/chief-of-staff style leader who took a product from prototype to a live pilot in 3 months, spanning public-sector data normalization, an ML matching engine, a secure API, and KPI/investor demo instrumentation. Strong focus on executive alignment and productivity via Notion-based operating systems plus automated reporting (Python/Power BI), with experience supporting fundraising and go-to-market narratives.”

“Backend engineer with experience modernizing high-traffic subscription and payment systems (TCS) by moving to event-driven Spring Boot microservices with Kafka, adding idempotency/state management to eliminate duplicate processing. Built and scaled FastAPI services for AI automation workflows (360DMMC) with versioned contracts, JWT security, and strong observability, and has led live refactors using feature flags, parallel runs, and data reconciliation.”

“Backend engineer who built a high-stakes, privacy-first platform at be Still Analytics for survivors of domestic violence, emphasizing anonymity, security, and reliability. Experienced with GenAI backends (LangChain + AWS Bedrock) including RAG to prevent hallucinations, plus cloud-native scaling (Docker/Kubernetes) and cost-saving migrations from legacy VMs to serverless (30% reduction).”

“Built and deployed a production LLM/RAG intelligent document understanding platform for healthcare clinical documents (notes, discharge summaries, diagnostic reports), integrating spaCy entity extraction, Pinecone vector search, and a Spring Boot API on AWS with monitoring and guardrails. Demonstrates strong MLOps/orchestration (LangChain, Airflow, Kubeflow/Kubernetes) and a metrics-driven evaluation approach, and partnered with a healthcare operations manager to cut manual review time by 80%.”

“Healthcare-focused ML/LLM engineer who built a production hybrid RAG workflow to automate prior authorization by retrieving from medical guidelines/historical cases (FAISS) and generating grounded rationales for clinicians. Strong in operationalizing ML with Airflow/Kubeflow/MLflow on SageMaker, optimizing latency (ONNX/quantization/async), and reducing hallucinations via evidence-only prompting; also partnered closely with clinical ops to deploy a readmission prediction tool used in daily rounds.”

“Security-focused engineer/customer-facing technical lead with SaaS platform experience at Ipsilon Lab, advising customers on API security and secure SDLC improvements. Has implemented production AppSec tooling (SAST/SCA), designed AWS least-privilege agent/scanning deployments, and led Kubernetes CI/CD security-agent integrations with Secrets Manager and PR gating. Strong track record troubleshooting complex customer integrations end-to-end (logs/metrics/traces through DB execution plans) and driving measurable stability/security posture improvements.”

“Backend engineer who evolved Coca-Cola bottlers' Trade Promotion Optimization platform at Coke One North America, building domain-focused microservices in Node.js and Python (Flask/FastAPI) with PostgreSQL. Experienced in multi-tenant security (OAuth2/JWT, RBAC, row-level scoping by bottler/region), API contract/versioning discipline, and Azure DevOps-driven incremental rollouts with strong observability.”

“Robotics software engineer with 2+ years of ROS/ROS2 experience who built a mobile robot stack from scratch (Fusion 360 → URDF → ROS) and integrated teleop, SLAM, and navigation. Worked in an ASU lab applying deep learning for person tracking on a TurtleBot setup, and solved real deployment issues like Raspberry Pi video-stream latency via compression and on-board processing. Also reports experience with CI/CD tooling (Jenkins) and Kubernetes.”

“Backend/ML engineer focused on clinical AI transparency who built ShifaMind, an explainability-enforced clinical ML system using UMLS/MIMIC-IV/PubMed data with RAG, GraphSAGE, and cross-attention. Demonstrated strong production engineering via FastAPI API design and safe migrations (feature flags/shadow inference), plus HIPAA-aligned auth/RLS patterns; also delivered a real-time comet detection system reaching 97.7% accuracy.”

“LLM engineer who built and deployed an emotionally intelligent AAC communication system using an emotion-aware RAG pipeline (Empathetic Dialogues + GoEmotions) and a PEFT-adapted model. Experienced with LangChain/LangGraph and custom Python orchestration, focusing on reliability (guards, schema validation, fallbacks), latency optimization, and rigorous evaluation (automatic metrics + human-in-the-loop), with a reported 18% user satisfaction improvement.”

“Robotics/AV-focused candidate who contributed to an F1TENTH autonomous vehicle college project, building key autonomy components from raw sensor data to driving commands. Strong in perception and state estimation (visual odometry, particle-filter localization), plus mapping (occupancy grids) and planning/control (RRT, Gap Follow, PID), with hands-on ROS tooling and simulation validation in Gazebo/RViz and ROS environment containerization using Docker.”

“Backend/Python engineer focused on real-time sensor/IoT analytics: built dashboards and a high-throughput ingestion pipeline (MQTT -> Python worker -> TimescaleDB) with buffering, batch inserts, and validation. Strong Kubernetes + GitOps practitioner (Dockerized microservices, HPA, probes, ArgoCD) who has handled production incidents like CrashLoopBackOff under peak load and supported an on-prem analytics migration to AWS using shadow traffic and rollback plans.”

“At Provenbase, built and shipped a production LLM-powered semantic search and candidate matching platform (RAG with GPT-4/Gemini, multi-agent orchestration, Elasticsearch vector search) to scale sourcing across 10M+ candidate records and 1000+ data sources. Drove sub-second performance, cut LLM spend 30% with routing/caching, and improved recruiting outcomes (+45% sourcing accuracy; +38% visibility of underrepresented talent) through bias-aware ranking and tight collaboration with recruiting stakeholders.”

“Built and productionized a domain-specific LLM-powered RAG knowledge assistant at JerseyStem for answering questions over large internal document corpora, owning the full stack from FAISS retrieval and LoRA/QLoRA fine-tuning to AWS autoscaling GPU deployment. Drove measurable gains (28% accuracy lift, 25% latency reduction) and improved reliability through hybrid retrieval, grounded decoding, preference-model reranking, and Airflow-orchestrated pipelines (35% faster runtime), while partnering closely with non-technical stakeholders to define success metrics and ensure adoption.”