Vetted PyTorch Professionals

“Applied AI/ML engineer with hands-on production experience building a RAG-based AI assistant for pharmaceutical maintenance troubleshooting using LangChain + FAISS/Pinecone, including a custom normalization layer to handle inconsistent terminology and duplicate document revisions. Also built Airflow-orchestrated pipelines for document ingestion/embeddings and predictive maintenance workflows (SCADA ETL, drift-based retraining), and partnered closely with production supervisors/quality engineers via Power BI dashboards and real-time alerts.”

“LLM/RAG engineer who has built and shipped production assistants, including a RAG-based teaching assistant (Marvel AI) using LangChain/LlamaIndex/ChromaDB with OpenAI embeddings and Redis vector search, achieving ~30% accuracy gains and ~35% latency reduction. Also deployed FastAPI services on Google Cloud Run with observability and prompt-level monitoring, and partnered with non-technical ops stakeholders to deliver an internal policy-document RAG assistant.”

“Full-stack engineer with payments/settlement domain experience who modernized a payment tracking workflow from REST to GraphQL and delivered a production payment status dashboard using Next.js App Router + TypeScript. Strong in performance and reliability work (Postgres indexing/Explain Analyze, Redis caching, Datadog observability) and in durable event-driven processing with Kafka (DLQs, idempotency, reconciliation, event replay).”

“GenAI/LLM engineer who built and productionized a 0-1 application (EMULaiTOR at Lumanity) combining qualitative + quantitative data using Postgres/pgvector RAG and prompt engineering, deployed with Azure backend and AWS-hosted frontend. Demonstrates strong production instincts (latency reduction via region alignment, autoscaling/health checks) and hands-on agent/tool-call debugging, plus experience enabling sales and winning a large pharma client.”

“Unreal Engine gameplay programmer with shipped experience on Five Nights at Freddy’s (including Ruin), spanning end-to-end systems like save/load + checkpoints, math-heavy spline-based AI movement, and player movement tuning. Also implemented a networked PvP dash using Unreal’s prediction pipeline (FSavedMove_Character) with server-authoritative validation, and has demonstrated strong debugging under stress-test conditions.”

“Full-stack engineer working at the intersection of product and infrastructure, building developer-facing interfaces for AI voice agents in XR/immersive environments plus telemetry-heavy analytics dashboards. Experienced in Postgres telemetry data modeling and performance tuning, and in designing durable multi-step LLM pipelines with idempotency, retries, and strong observability; has operated in fast-moving startup-like teams (Biocom, HandshakeAI).”

“Built and shipped both a solo real-time multiplayer Spades game (TypeScript monorepo with shared client/server engine) and a production internal LLM-powered document Q&A tool for a SaaS company. Demonstrates strong RAG pipeline design (Pinecone + embeddings + reranking), rigorous eval/regression practices, and pragmatic data ingestion/observability work across Confluence, Notion, and messy PDFs/OCR—backed by clear metric improvements (P@1 61%→78%, escalations 40%→22%).”

“Robotics software engineer with ~4 years of ROS experience who implemented a real-time diffusion-policy control loop entirely in Gazebo, focusing on inference-latency reduction (warm-start + truncated denoising) for stable closed-loop execution. Has hands-on experience building custom ROS control nodes, optimizing AMR navigation (SLAM + RRT) with sensor-fusion for dynamic obstacles, and designing deterministic multi-robot coordination; also uses Dockerized ROS environments and automated simulation/benchmark pipelines.”

“Backend/data engineer focused on AWS-based, low-latency event processing for market data and social-signal sentiment systems. Has led a monolith-to-event-driven migration with feature-flagged incremental rollout, and emphasizes production-grade security (OAuth2/JWT, secrets management, Supabase RLS) and data integrity (deduplication/idempotency) under high-volume spike conditions.”

“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-powered internal AI assistant using a RAG pipeline to help teams search internal PDFs/knowledge bases and generate grounded summaries/answers. Demonstrates strong end-to-end ownership (ingestion through APIs) plus production rigor (monitoring/logging/CI-CD, evaluation metrics) and practical optimizations for hallucination, latency, and answer quality (thresholding, fallbacks, caching, async, re-ranking, two-tier model routing).”

“Built and shipped a production real-time content moderation platform for Zoom/WebEx-style meetings, combining Whisper speech-to-text with fast NLP classifiers and REST APIs to flag hate speech, bias, and HIPAA-related content under strict latency constraints. Demonstrates strong MLOps/infra depth (Airflow, Kubernetes, Terraform/Helm, observability) and a pragmatic approach to reducing false positives via threshold tuning, context validation, and hard-negative data—while partnering closely with compliance and product stakeholders.”

“Built and deployed a production LLM-driven document understanding pipeline using LangChain/LangGraph, focusing on reliability via step-by-step prompting, validation checks, and monitoring. Also partnered with non-technical marketing stakeholders at Heartland Community Network to deliver an XGBoost targeting model surfaced in Power BI, improving campaign conversion by 12%.”

“At ReferU.AI, designed and deployed an agentic RAG pipeline that automates multi-jurisdiction legal document drafting, emphasizing hallucination reduction through hybrid retrieval, validation agents, guardrails, and iterative regeneration. Experienced with orchestration frameworks (especially CrewAI) and rigorous testing/evaluation practices including human-in-the-loop review, adversarial testing, and production metrics/logging.”

“Autonomy Engineer at Joulea Inc (Atlanta) with ~3 years building a drone autonomy stack end-to-end, spanning controls, swarm path planning, SLAM/LIO, and multi-sensor fusion (lidar/IMU/GPS RTK/camera). Notable work includes lidar degeneracy detection using Hessian-based constraints in an EKF and fusing visual odometry to reduce drift, plus ongoing lidar-camera synchronization and calibration.”

“Developed and productionized VL-Mate, a vision-language, LLM-powered assistant aimed at helping visually impaired users understand their surroundings and query internal knowledge. Emphasizes reliability and safety via confidence thresholds, uncertainty-aware fallbacks, hallucination grounding checks, and rigorous offline + user-in-the-loop evaluation, with experience orchestrating multi-step LLM pipelines (LangChain-style and custom Python async) and deploying on containerized infrastructure.”

“Robotics/ML engineer who benchmarks and adapts open-source robot action models, building synthetic datasets in Isaac Sim and modifying vendor code to scale training across multiple GPUs. Also built a production-style computer vision pipeline at Zortag—training a tiny YOLO-based classifier for fake-vs-real label detection and deploying it in a real-time iOS app with additional display/spoof detection.”

“Robotics project manager and software lead for an underwater ROV (MATE 2024–2025), building a ROS 2 Jazzy stack on Raspberry Pi and a serial Pi-to-Arduino thruster control system for a 6-thruster configuration. Also has internship experience creating automated functional test pipelines using Jenkins, Selenium, and Python, plus exposure to Isaac Sim for simulated/synthetic data generation in an embodied AI hackathon.”

“Built and deployed a production AI invention/patent review platform that compares invention submissions against patent rules to provide instant feedback, reportedly cutting legal team review time by ~80%. Learned Snowflake Cortex LLMs and production deployment (Docker + AWS) on the job, and validated system quality through human-in-the-loop testing with experienced legal stakeholders.”

“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.”

“UE5 gameplay scripter (Blueprint-focused) from small startup teams who specializes in setting up core project architecture (game loop/data flow, controller, game instance, UI/menu) and building modular systems reused across multiple games. Rebuilt a save-system architecture from scratch to replace a problematic asset and improve scalability/maintainability, and has hands-on performance optimization experience (profiling, level streaming, Nanite-to-LOD tradeoffs). Also implemented a realistic cricket match simulation using Markov chains and transition matrices.”

“Machine-vision engineer who designed an end-to-end inline inspection station for white wood pallets, combining laser line profilers with 2D color line-scan imaging to detect protruding nails (~2mm threshold) at conveyor speeds. Solved real production constraints (lighting reflections, per-trigger depth/color alignment, barcode tracking) and improved system accuracy from ~80% to 99.5% using barcode symbology changes and Keyence reader AI features.”

“Full-stack/AI engineer who has shipped production systems spanning real-time analytics dashboards and an internal LLM-powered knowledge assistant. Experienced with RAG pipelines (embeddings/vector DB, semantic retrieval, query rewriting) plus evaluation loops and guardrails, and builds observable Kafka-based data pipelines monitored with Prometheus/Grafana.”