Vetted Machine Learning Professionals

“Full-stack developer who built an end-to-end e-commerce application with admin/blog/announcement features using Node/Express and AWS S3, emphasizing security via expiring presigned URLs. Also has strong distributed-systems fundamentals from implementing the Raft consensus algorithm (replication logs, majority acks, leader elections) and has created build automation tools (GNU Makefiles/scripts) to streamline team workflows.”

“Built and productionized a real-time social-media sentiment analysis system used by a marketing team to monitor brand/campaign performance. Experienced in orchestrating LLM workflows with LangChain (validation → prompting → parsing → post-processing), plus monitoring, retraining, and RAG-style retrieval using embeddings/vector stores to keep outputs reliable over time.”

“Frontend engineer who has led end-to-end builds of complex React + TypeScript workflow editors (multi-step scenario builder with nodes/connections/conditions) with strong quality practices (CI/CD, unit tests, schema validation, logging, feature flags). Also delivered an AR flower-placement feature during an internship at Ecomspiders, rebuilding the experience with Three.js, live camera preview, and surface placement tested across devices and lighting conditions.”

“AI engineer who built an agentic, production-deployed LLM workflow for tobacco violation parsing and automated multi-case creation, using six specialized agents and a human-in-the-loop confidence-threshold routing design. Addressed data privacy constraints by generating synthetic datasets with LLM prompting, and orchestrated reproducible end-to-end pipelines in LangChain with robust testing and evaluation (precision/recall, micro-F1).”

“ML practitioner who built a production customer risk scoring system to replace slow manual approvals, owning the full pipeline from feature engineering and XGBoost training to deploying a Dockerized FastAPI prediction service. Emphasizes reliability and business-aligned evaluation (recall/ROC-AUC, threshold tuning, drift monitoring) and is comfortable translating model decisions into stakeholder metrics like conversion rate (experience at EasyBee AI).”

“Unity/VR gameplay programmer who delivered core VR locomotion and a custom collision system for the VR port of Tinhearts, enabling comfortable movement in highly detailed, cluttered environments and improving cutscene reliability. Also has professional VR multiplayer experience on Project Leeroy using Photon Fusion (plus PUN/SpatialOS/UNet/WebSockets), including building drag-and-drop networking components to help designers ship minigames faster.”

“Robotics engineer who built a real, mostly self-assembled autonomous robot (WRAITH) as a final-year project, implementing ROS2-based 2D SLAM (Cartographer/SLAM Toolbox) and Nav2 on a Raspberry Pi 5 under tight CPU/RAM and OS compatibility constraints. Also delivered a full Flutter mobile control app backed by a Flask REST API (manual control, live camera streaming, mapping/navigation) and introduced an image-based verification method to improve localization.”

“Cybersecurity-focused candidate whose master’s thesis performed an empirical, systematic study of memory and disk forensics behavior across Windows and Linux under Trojan and ransomware conditions. Has experience optimizing and debugging large, real-time data-processing pipelines (e.g., memory dumps/logs) and using Docker for forensic analysis workflows; no robotics/ROS experience to date.”

“Built and productionized LLM-powered workflows that generate contextual insights from structured financial data, including prompt/retrieval design, data standardization, and reliability controls like rate limiting and batching. Also diagnosed and fixed real-time failures in an automated order validation system using logs/metrics, staging reproduction, edge-case handling, retries, and alerting, while supporting sales/customer teams with demos, scripts, and FAQs to drive adoption.”

“Data/NLP practitioner who builds production-oriented pipelines for unstructured text: entity extraction, topic modeling (LDA/BERTopic), and semantic search using Sentence-BERT embeddings with FAISS. Emphasizes rigorous evaluation (coherence/silhouette + manual review), entity resolution with validation, and scalable workflow orchestration using Airflow/Prefect with Spark/Dask.”

“Backend engineer with production ERP experience deploying microservices and improving performance/reliability using a metrics-driven approach (logs, latency, error rates). Has hands-on cloud/hybrid operations across AWS and Azure with Docker/Kubernetes, and has resolved real-world mobile sync issues by tuning timeouts/retries and reducing payload sizes. Builds configurable Python services to deliver customer-specific behavior without destabilizing the core codebase.”

“Machine learning lab assistant at Eastern Illinois University who productionized a voice-enabled conversational AI system: redesigned it with RAG, LoRA fine-tuning (including text-to-SQL), and safety guardrails, then deployed a scalable API supporting ~1,000 daily queries. Also partnered with customer-facing teams during a BlueFi internship by building demos/APIs and accelerating releases via Terraform + AWS CI/CD automation.”

“Backend/integration-focused software engineer who built and debugged a complex location modeling system (data pipelines, APIs, optimization logic connected to a dashboard). No direct ROS/robotics experience yet, but demonstrates strong distributed-systems debugging, containerized deployment (Docker), and CI/CD/testing practices and is actively looking to pivot into robotics software.”

“Software engineer with demonstrated production performance wins (37% latency reduction) through SQL optimization, backend API redesign, and disciplined rollout practices (staging, feature flags). Experienced debugging distributed pipeline issues across infrastructure layers (memory pressure and network timeouts) and building AWS-based systems (Lambda + RDS) to handle request spikes, including work on a business-focused chatbot.”

“Built a voice-driven desktop assistant for users with mobility impairments, integrating Whisper and Google Gemini and adding voice-authentication via speaker embeddings for secure command execution. Has hands-on experience with AWS serverless/microservices patterns (Lambda, S3, CloudFront, CloudWatch) and CI/CD, plus built an internal MySQL-to-MongoDB migration tool used by the CTO and dev team with an emphasis on safe, low-impact data transformation.”

“ML/NLP engineer who has built production systems end-to-end: a real-time recommendation platform (100k+ profiles) using BERTopic-style clustering and a RAG-based news summarization/recommendation stack with ChromaDB. Strong focus on scaling and reliability (GPU batching, Redis caching, Kafka ingestion, Docker/Kubernetes, Prometheus/Grafana) and on maintaining model quality over time via drift monitoring and retraining triggers.”

“Unity developer with strong real-time 3D and mobile performance optimization experience (rendering, memory, GC), building interactive environments and physics-based interactions. Built a mobile Unity game prototype largely solo end-to-end, emphasizing state-driven architecture and clean separation between game logic, UI, and data—skills that translate directly to AR/VR despite no shipped Meta Quest titles.”

“Built a university campus assistant chatbot (BabyJ/WWJ) using RAG and agentic routing with a FastAPI + React stack and JWT auth, focusing heavily on production concerns like latency and reliability. Uses techniques like speculative prefetching, smart intent routing, and rigorous eval/testing (golden sets, regression, edge cases) while collaborating closely with campus admin/advising teams to iterate based on real user feedback.”

“Research Analyst at Syracuse who deployed an LLM-powered lab automation system allowing researchers to run QCoDeS instrument workflows via natural language, with strong safety guardrails for real instruments and multi-instrument support. Also collaborated with non-technical stakeholders at iConsult on an audio classification/recommendation pipeline, translating business goals into metrics and Tableau dashboards with model comparisons and A/B test results.”

“Backend/ML engineer with healthcare domain experience building secure Medicare/Medicaid data APIs and real-time patient risk scoring. Shipped an end-to-end ML pipeline (scikit-learn/XGBoost) served via SageMaker and integrated into Flask APIs, with strong production reliability practices (Kafka schema validation, regression replay, observability, drift monitoring, and human-in-the-loop guardrails).”

“Backend/ML engineering candidate focused on fintech automation who architected a zero-to-one agentic/LLM-enabled system to reconcile messy financial documents and bank transactions, reporting ~40% operational efficiency gains. Experienced migrating monoliths to event-driven microservices with incremental rollout via reverse proxy, and implementing production-grade security (OAuth2/JWT, RBAC, Supabase RLS) plus resilience patterns (timeouts/retries under concurrency).”

“Unity VR developer who has shipped Meta Quest titles while owning full end-to-end delivery: gameplay, UI, database/backend, blockchain, web integrations, IAP, and final build publishing. Notably solved a Meta SDK IAP callback regression by identifying the issue with Meta support and safely rolling back specific SDK code, and has implemented motion/walking tracker systems with ML-driven opponent behavior.”

“Internship-built full-stack systems spanning HR employee-record portals and internal data-quality dashboards (Flask + SQL + React), emphasizing data integrity and rapid MVP iteration. Also implemented Flask microservices with RabbitMQ for distributed task processing, addressing duplication/ordering issues with idempotency, durable queues, and correlation-ID logging; delivered quantified productivity gains for HR teams.”

“Built an end-to-end car maintenance application from design through deployment using TypeScript/React with a Node.js + Firebase backend, including OBD integration. Uses mock servers and a layered frontend architecture (UI/state/services separation) to iterate quickly on UX without being blocked by backend or hardware dependencies.”