Vetted Machine Learning Professionals

“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.”

“Full-stack intern at Rebel who owned backend work on a cross-platform music platform using Python/Django with MongoDB, implementing user-focused REST APIs end-to-end. Also built CI/CD pipelines (Jenkins/GitHub Actions) to containerize and deploy to AWS, and has experience integrating Kafka-based real-time event processing with reliability and observability practices.”

“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.”

“Built an OCR backend that trains a custom Tesseract model for proprietary fonts and scales via multi-tenant isolation (tenant-scoped APIs, per-tenant storage, JWT+RBAC). Improved high-load image processing by shifting OCR to async worker queues and adding Redis caching, cutting processing time by ~66%, and also integrated Claude API to auto-generate test cases on code changes.”

“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%.”

“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 a production lead-generation system using AI agents that researches the internet for relevant leads and integrates RAG-based contact enrichment/shortlisting aligned to existing CRM data, enabling sales reps to focus more on selling. Also has hands-on AWS data orchestration experience (Glue, Step Functions) moving raw data into Redshift and evaluates agent performance with human-in-the-loop plus BLEU/perplexity metrics.”

“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.”

“Co-founder at Skarbe who built “Oskar,” an autonomous sales agent that handles inbound email, lead qualification, and automated follow-ups using a multi-agent architecture (OpenAI Agents SDK) with a human-in-the-loop learning phase for reliability. Also owned scaling and reliability of Gmail/Outlook sync, including a Node.js-to-Rails migration and incident response during a Product Hunt launch that generated hundreds of signups and a ~2M-email queue.”

“Data/ML engineer from HCLTech who modernized enterprise data by linking fragmented financial and supply-chain data across SAP/SQL Server/Snowflake using NLP entity linking and embeddings (FAISS). Delivered measurable impact including ~40% reduction in manual error-log triage and entity-linking accuracy improvements from ~86% to ~93%, with results surfaced in Power BI for real-time analytics.”

“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.”

“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.”

“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.”

“B2B product builder with prior experience taking products from 0-1 and scaling to revenue; previously implemented e-commerce search and turned it into a monetized paid-results/bidding platform requiring architectural changes and GTM alignment. Now exploring a startup idea focused on retention and upsell for B2B companies, targeting the underserved long-tail partner segment and already validating the gap with industry leaders and POC conversations.”

“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.”

“Built core perception and decision layers for a 3D AI-powered interactive avatar/agent with a robotics-like perception–reasoning–action loop, combining computer vision, NLP, and real-time response. Focused on making multimodal inputs robust (normalization, intent + emotion signal fusion) and improving real-time performance via instrumentation, profiling, and parallelization; also designed distributed, loosely coupled state-based communication and deployed services with Docker.”

“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.”

“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/data engineer with hands-on production experience across Azure and AWS: built FastAPI + PostgreSQL services with Azure AD OAuth2/JWT auth and strong reliability patterns (timeouts, retries, correlation IDs). Delivered AWS Lambda/ECS solutions with Terraform/CI-CD and cost controls (SQS buffering, reserved concurrency), and built/operated AWS Glue ETL pipelines into Redshift while modernizing legacy SAS reporting into Python microservices with parity testing.”

“Backend engineer with legal-tech and AI workflow experience: built JurisAI, an end-to-end legal research system using OCR + embeddings + Pinecone vector search to deliver citation-grounded LLM answers with safe failure modes (~90% recall@K). Also led a GW Law metadata migration into Caspio with batch validation and parallel rollout, and has strong FastAPI/GCP production reliability and observability practices.”

“Unity gameplay/networking engineer who built a data-driven 3D auto-chess grid system with strong designer-facing configurability and decoupled event-bus integration. Previously worked on Stranded Deep using Photon Bolt, fixing multiplayer synchronization issues affecting millions of PC/console players (reliable RPC approach). Also brings VR/mobile optimization experience (reported ~10x FPS gain via baked lighting) and mentors juniors on MVP/reactive UI patterns.”

“Full-stack engineer who built an AI-based inventory/procurement query system at Botlily/Botlerly using Flask and Google Sheets as a live knowledge base, overcoming Sheets latency with caching and structured in-memory models. Demonstrated strong LLM product engineering (40% accuracy improvement via preprocessing/prompting) and customer-driven iteration with bar/restaurant owners, evolving the tool into a more comprehensive inventory management and forecasting solution.”

“Built a production Apple-focused LLM Q&A bot that answers user issues using similar past discussion records, including large-scale scraping and cleaning of thousands of forum threads. Used BeautifulSoup + Playwright for static/dynamic extraction, PySpark + NLP for preprocessing, and LangChain RAG with a custom response-likeliness metric to evaluate performance.”