Vetted C++ Professionals

“Healthcare-focused LLM engineer who has built two production clinical applications: an automated structured clinical report generator from physician-patient conversations and a RAG-based chatbot for retrieving patient history (procedures, allergies, etc.). Demonstrates strong applied RAG expertise (overlapping chunking, entity dependency graphs, temporal filtering, graph RAG) to reduce hallucinations/omissions and partners closely with clinicians to automate hospital workflows.”

“Backend/data engineer with production experience building secure Django/DRF APIs (JWT RS256 + rotating refresh tokens), background processing with Celery, and strong reliability practices (timeouts, retries/backoff, structured logging, audit trails). Has delivered AWS solutions spanning Lambda + ECS with IaC/CI-CD and built Glue/PySpark ETL pipelines with schema evolution and data-quality quarantine patterns; also modernized a legacy SAS pipeline to Python/PySpark with parallel-run parity validation and phased rollout.”

“Generalist troubleshooter with experience debugging across C (memory leaks with Valgrind), Python (bug tracing to meet deadlines), and VHDL (hardware-related issues), plus some exposure to production deployments and networking issue resolution. Has supported customers remotely and emphasizes calm, patient communication during incident resolution.”

“AI/LLM engineer who built a production content-generation system for nursing education, combining multimodal RAG over proprietary PDFs (including images) with structured Cosmos DB data and external sources. Strong focus on production reliability—prompt-chaining with LangChain, validation/guardrails, and Azure-based monitoring/observability—plus experience designing Azure AI agents with tool integrations like Bing Search.”

“Unity developer with deep GPU compute experience who shipped a web-deployed CAD-style app requiring real-time mesh manipulation, solving performance and browser memory-limit issues via compute shaders and mesh chunking. Built an independent Unity gravity simulation using Schwarzschild approximation and geodesic integration, and has also worked on game-engine threading/job-queue architecture using AI-assisted workflows.”

“Frontend-focused engineer who built a full movie recommendation system from concept to production, comparing classic collaborative filtering with LLM-based recommendation approaches on AWS. Emphasizes scalable architecture, strict TypeScript data contracts, and high-quality Next.js/React UI patterns (defensive states, scoped state management, performance optimization) with disciplined QA and feature-flagged rollouts.”

“Former Field Application Engineer at TestTorrent supporting proprietary hardware deployments. Built and integrated customer-requested benchmarking (including MLPerf-style work) by adapting GPU benchmark implementations and validating performance against NVIDIA baselines, plus delivered a backend fix that enabled customer performance testing and helped close a deal. Also has experience running models on AWS/GCP with multi-instance cost optimization techniques like memory offloading.”

“Founding AI engineer (June 2024) at Talon Labs who built and productionized an LLM-powered chatbot for interacting with proprietary supply-chain documents, deployed at large scale (25–100,000 users). Experienced with RAG/LLM orchestration (LangChain, LlamaIndex, Groq AI) and production ops tooling (Kubernetes, Docker, Kubeflow, Airflow), with a metrics-driven approach to evaluation, observability, and stakeholder alignment.”

“ML practitioner with hands-on experience adapting diffusion models (DDPM + U-Net in PyTorch) to improve low-dose CT medical imaging quality via denoising and upsampling against high-dose ground truth. Also built a RAG workflow during a recent internship by cleaning client survey data, embedding with OpenAI text-embedding-3-large, and indexing in Pinecone with MD5 deduplication, alongside a strong emphasis on production-grade Python practices.”

“Open-source contributor who improved React Query’s caching/subscription behavior to reduce unnecessary re-renders via debouncing and batched updates, validated with benchmarking and extensive tests. Also maintained a Flask extension and resolved production background-task hangs by tracing Redis connection handling issues, adding cleanup/retry logic and troubleshooting docs. In a fast-paced startup, owned the design of a Celery+Redis multi-queue background processing system with Prometheus-based observability.”

“Backend-focused Python/Flask engineer who owned REST APIs for a video analysis system, including preprocessing, ML inference integration, and post-processing into time-aligned predictions consumable by a React UI. Demonstrated practical performance/scalability work by decoupling API request handling from CPU-heavy processing and adding timing instrumentation to identify and optimize bottlenecks.”

“Robotics software engineer with deep ROS/ROS 2 autonomy experience across warehouse fleets (Knapp delivery robots and quadrupeds), spanning SLAM, EKF-based sensor fusion localization, Nav2, and behavior-tree mission orchestration. Built a simulation-first testing approach using Isaac Sim Replicator with Dockerized, statistically analyzed repeat runs to catch nondeterminism, and personally owned real-world validation. Also developed a custom UR10 singularity-check ROS node based on manipulability.”

“Backend engineer with strong Python/FastAPI microservices ownership, including an ML-serving service with embeddings, async DB access, and Redis caching to reduce latency under high load. Experienced deploying and operating containerized services on Kubernetes using GitOps (Argo CD/Helm) with automated CI/CD, plus hands-on Kafka streaming pipeline tuning and enterprise migration work (Infosys) using blue-green/active-passive strategies.”

“Finance-domain ML/LLM engineer who has shipped production systems including a RAG-based financial insights assistant with a custom post-generation validation layer that verifies atomic claims against retrieved source text to prevent hallucinations in compliance-critical workflows. Also built large-scale MLOps automation on AWS using Kubeflow + MLflow + CI/CD for fraud detection and credit risk models processing 500M+ transactions/day with a 99.99% uptime goal, and partnered closely with JP Morgan risk/compliance stakeholders on NLP-driven compliance monitoring.”

“PayPal ML/AI practitioner who built and productionized a hybrid recommendation engine (BERT/LLM embeddings + collaborative filtering + XGBoost ranking) on AWS with end-to-end MLOps and orchestration. Addressed real-world issues like cold start and embedding latency (ONNX, clustering, caching, PySpark/Delta Lake) and drove a 27% lift in upsell conversion via A/B testing and stakeholder collaboration with marketing.”

“Backend engineer with strong performance and security instincts: built a Flask API for readability metrics with clean, testable modular design; optimized SQLAlchemy/Postgres to eliminate N+1 issues (800ms to 120ms). Also implemented an LLM-powered natural-language travel search using Claude Sonnet + Amadeus with RAG and anti-exploitation safeguards, plus multi-tenant isolation via Postgres RLS and Redis caching that cut search latency from ~20s to ~4–5s while reducing storage costs.”

“Robotics-focused engineer with co-op experience building and debugging embedded C++/Python drivers for time-of-flight sensing on a Flex Stacker product, plus automation of large-scale test data collection via Google Drive/Sheets APIs to enable parallel robot testing. Also has ROS2 sensor-driver experience (GPS/RTK/IMU with custom messages/ROSbags) and is building a side project integrating Whisper-based live transcription with chunked abstractive summarization in a latency-aware pipeline.”

“Forward Deployed Engineer focused on taking enterprise LLM voice agents from prototype to production. Led a turnaround on a high churn-risk account by building a custom nested-API integration and preprocessing layer that enabled the LLM to reason over complex order hierarchies, cutting call handle time from 15 minutes to 2 minutes and driving expansions. Strong in real-time agent/workflow debugging, developer workshops, and sales partnership for adoption.”

“Backend/AI workflow engineer with experience at AirKitchenz, Uber, and Vivma Software, building production systems on AWS (Lambda, DynamoDB, Step Functions). Has a track record of major performance wins (DynamoDB latency 2s to <150ms; Postgres query 2s to ~180ms) and shipping LLM-powered onboarding and ticket-routing workflows with strong guardrails (schema validation, confidence thresholds, human-in-the-loop escalation).”

“Currently a startup CTO who thrives on building new technology stacks and rapidly turning technical ideas into products. Interested in partnering with a CEO/business team to commercialize embedded/edge concepts such as multi-sensor drone localization (video/audio/RF with SDR), low-cost solar+battery power nodes networked via LoRa, and an Amazon Sidewalk/LoRa connectivity device with cloud management.”

“Full-stack engineer who built and owned an AI-powered personal statement editor in Next.js (App Router + TypeScript), including dynamic routing, server-side data fetching, and typed API route handlers. Post-launch, they handled production monitoring/debugging and shipped reliability/performance upgrades (rate limiting, retries, rollback, DB indexing), and report a 40% latency reduction using Suspense/streaming and React concurrency patterns. Also implemented a durable Temporal-orchestrated AI document workflow with robust retry/idempotency strategies.”

“ML/LLM practitioner who has deployed a production RAG-based trouble-call identifier using multiple datasets (device, network, past complaints). Experienced in end-to-end MLOps (FastAPI + Docker + Kubernetes with HPA) and in evaluating/monitoring LLM behavior to reduce hallucinations, with additional applied work in forecasting/anomaly detection and churn prediction for retention campaigns.”

“Robotics software candidate with experience spanning simulation (MuJoCo, Gazebo, Webots) and ROS1/ROS2 development, including hardware-oriented work on a hexapod and a Mecademic Meca500 R3 arm. Built a visually guided interactive indoor robot system using a CV pipeline plus POMDP + imitation learning with PPO-based residual RL, and has practical debugging experience improving LiDAR SLAM stability and migrating sensor interfaces from ROS1 to ROS2.”

“At Pitt, built and productionized a deep-learning (LSTM) surrogate thermal solver integrated into the ExaCA simulation pipeline for NASA partner teams, enabling same-day parametric studies (16x speedup, ~5% error) with guardrails and FEM-based validation. Presents this work at major conferences (SFF 2023, TMS 2024) and emphasizes practical, end-to-end workflows and reliability over paper accuracy.”