Vetted Deep Learning 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.”

“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.”

“Built and productionized a multi-agent, LLM-powered document understanding system to replace manual review of long documents, using LangGraph orchestration plus RAG to reduce hallucinations. Implemented layered reliability controls (structured templates, checker agent, and human-in-the-loop feedback) and reported ~40% speed improvement after orchestration; also has hands-on Airflow experience for scheduled data pipelines.”

“LLM/ML platform engineer with hands-on experience taking an LLM document summarization prototype into a production-grade service on AWS EKS, emphasizing low-latency inference, drift monitoring, and safe CI/CD rollouts (canary + rollback). Strong in real-time debugging of agentic/RAG systems (tracing, retrieval/index drift fixes) and in developer enablement through practical workshops (Docker/Kubernetes/FastAPI) plus pre-sales support via demos and benchmarks to close pilots.”

“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.”

“Senior technology leader and hands-on builder spanning enterprise architecture and product/engineering leadership across healthcare and entertainment. Has led high-impact cloud and security architecture decisions (including establishing a private cloud to address scalability/security at massive scale) and scaled orgs 300% using pod-based team structures. Currently building an AI-supported hydroponics/vertical farming IoT framework (ESP32 + Azure) and a musician collaboration platform (React + Neo4j + AWS).”

“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.”

“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.”

“LLM/GenAI engineer with US Bank experience building a production financial-document intelligence platform using LangChain/LangGraph, GPT-4, and Amazon OpenSearch. Delivered a RAG-based assistant for compliance/audit teams with grounded, cited answers, focusing on reducing hallucinations and latency, and deployed securely on AWS (SageMaker/EKS) with CI/CD and evaluation tooling (LangSmith, RAGAS).”

“Built and shipped LLM/RAG systems in finance and startup settings, including a Goldman Sachs document intelligence platform that indexed ~8TB of regulatory filings and delivered cited, conversational answers with <2s latency—cutting compliance research by ~4.5 hours per batch. Also developed LangChain-based agent workflows at Finta to automate CRM enrichment and investor lookup with strong testing, tracing (LangSmith), privacy guardrails, and auditability.”

“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 engineer who has deployed a production LLM-powered assistant for intent classification and query routing (order recommendation/support deflection), combining BERT fine-tuning with an embedding-based retrieval layer and optimizing for low-latency inference. Experienced with end-to-end reliability practices—Airflow-orchestrated ETL, data validation/alerting, MLflow experiment tracking, and iterative improvements driven by user feedback and monitoring.”

“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.”

“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.”

“Entrepreneur building an applied-AI tool for geological resource exploration (critical minerals, oil & gas) that overlays proprietary and public data from reports/logs/maps to generate evidence-based greenfield profiling insights. Has spent ~2 years on industry research, built a POC, validated demand with purchasing signals, and developed partnerships/network including USGS, DARPA, and ESRI.”

“Backend engineer with experience evolving a high-volume agricultural loan processing platform (APMS) at HDFC Bank, emphasizing transactional integrity, auditability, and modularity while integrating with credit bureaus, document management, and risk engines. Also improved automation/reporting robustness at Trend Micro by catching duplicate-event retry edge cases and adding idempotency safeguards.”

“Internship experience at Qualitest building and deploying an LLM-powered test automation system that reduced manual test creation and improved efficiency (~40%). Demonstrates strong production engineering for LLM systems (timeouts/retries/monitoring/caching, prompt optimization, batching) and has scaled workflows to 100+ concurrent jobs; also has orchestration experience with AWS Step Functions and Kubernetes.”

“Robotics software engineer with hands-on ROS2 + Autoware perception experience, focused on building benchmarking infrastructure for object detection models inside a real-time autonomous driving stack. Strong in evaluation rigor (synchronization, deterministic playback, format standardization) and practical ROS2 debugging/validation workflows using RViz and Gazebo.”

“Built and deployed a production agentic LLM knowledge assistant that answers complex questions over internal documents, APIs, and databases using a RAG architecture (FAISS/Pinecone) and LangChain/LangGraph orchestration. Emphasizes production-grade reliability and hallucination control through grounding, confidence thresholds, validation, retries/fallbacks, and full observability (logging/metrics/traces) with continuous evaluation and feedback loops.”

“At Inertia Systems, built a production LLM-powered ingestion pipeline that converts heterogeneous sources (PDF/JSON/IFC/SQL and financial tables) into standardized text and uses GraphRAG to construct a knowledge graph with verified dependency relationships. Also has hands-on HPC orchestration experience with SLURM, including creating a custom wrapper process manager to improve resource utilization under restrictive scheduling policies.”

“Open-source JavaScript contributor focused on performance and maintainability in data visualization libraries—refactored legacy ES5 into modular ES6, added tests/docs, and delivered ~30% faster load times with positive community adoption. Also optimized a React dashboard (~40% load-time reduction) and took ownership in an ambiguous AI product initiative by setting milestones, standing up an initial ML pipeline, and shipping a prototype in ~6 weeks that became the basis for production.”

“Worked in a startup building an automated carbon accounting/climate reporting product, partnering with client IT and internal cross-functional teams to ship features and train end users. Also has software engineering internship experience debugging complex multi-workflow systems, including uncovering a significant (~20%) data annotation error by instrumenting and testing each workflow step.”

“Built a production-oriented multimodal RAG "Fix Assistant" with FastAPI, Tavily search, BM25 + cross-encoder reranking, and a local Phi-3.5 model, emphasizing strict grounding and fallback/verification modes to prevent hallucinations. Also has hands-on federated learning experience using STADLE to orchestrate edge-node training and aggregation for EV telemetry data, plus experience communicating AI results to non-technical stakeholders (traffic RL/congestion outcomes).”