Vetted Machine Learning Professionals

“Recent McCombs School of Business (UT Austin) Post Graduate Program graduate in Data Science & Business Analytics with hands-on project experience spanning stock clustering/segmentation and hotel booking-cancellation prediction. Strong in end-to-end analysis workflows (EDA, cleaning, feature engineering) and rigorous model comparison/selection, with exposure to boosting methods and imbalanced-data techniques; limited experience so far with embeddings/vector databases and production deployment.”

“Product engineer/designer with founder mindset who shipped a blockchain-enabled investor group/governance platform using Next.js (App Router), TypeScript, Prisma/Postgres, and Temporal. Emphasizes auth-centric onboarding (SSO + embedded wallet) to make dApp UX feel more like SaaS, and brings strong reliability practices (idempotent retries, reconciliation) plus experience demoing to investors and operating in seed-stage teams (ex-Vouched).”

“Robotics intern at the University of Delaware who built and debugged ROS2-based multi-robot coordination systems, focusing on real-time reliability (timestamp alignment, latency/jitter instrumentation, QoS/executor tuning). Also improved SLAM stability by fixing LiDAR/encoder synchronization and tuning state-estimation parameters, with a simulation-first workflow using Gazebo and Docker/CI for reproducible deployments.”

“Built and shipped a production RAG system (“TROJAN KNOWLEDGE”) for answering questions over technical PDFs, using a 3-stage retrieval stack (BM25 + FAISS + cross-encoder) to lift F1 from 71% to 84%. Drove major performance gains with a 3-level cache (memory/Redis/disk) cutting latency from ~200ms to ~10ms, and added Prometheus/Grafana monitoring plus LangChain-based fallback logic to handle OpenAI rate limits under load.”

“AI/backend engineer who fine-tuned and deployed a production LLM chatbot using a LangChain + FAISS RAG pipeline, improving latency with PEFT/LoRA and driving strong business impact (40% customer adoption; 92% satisfaction). Also served as technical lead on a data aggregation system for underwriting/quoting, introducing GraphQL for more efficient, maintainable querying and applying CDC to keep cached ranking data fresh at scale.”

“MS candidate with strong backend/data engineering focus who builds research and data systems with production-grade rigor (reproducibility, observability, restartability). Has hands-on experience securing and scaling FastAPI-based gateways in front of Java microservices, leading SQL Server→Snowflake migrations with dual-write/feature-flag rollouts, and hardening Kafka-based fleet-tracking systems against out-of-order and duplicate events.”

“Built a production RAG chatbot for Worcester Polytechnic Institute that indexes 500+ webpages using FAISS + Llama 3, with strong grounding/hallucination controls (confidence thresholds and citations). Also has internship experience orchestrating multi-step ETL pipelines with AWS Step Functions and delivered a 30x faster fraud/claims triage workflow at Munich Re using association rules and stakeholder-friendly dashboards.”

“Built and deployed a production RAG pipeline at PNC Financial Services to let risk/compliance analysts query millions of internal financial documents in natural language, reducing manual search and speeding regulatory validation. Demonstrates deep practical experience with large-scale document ingestion/OCR cleanup, retrieval performance tuning (hierarchical indexing, caching), and LLM reliability controls (grounding, citations, abstention), plus cloud orchestration on Azure and AWS.”

“Aspiring founder with ~15 years of experience across varied backgrounds, motivated by frustration with slow, change-resistant large organizations and a desire to bring innovative ideas to market. Familiar with how venture capital/accelerators function (though not directly worked in them) and expresses strong willingness to take entrepreneurial risks.”

“AI/LLM engineer with healthcare domain experience who built a production clinical support “chart bot” for Molina, including PHI-safe ingestion of 200k+ PDF policies, vector retrieval, and a fine-tuned LLaMA served via vLLM on ECS Fargate. Demonstrated measurable performance wins (HNSW + namespace partitioning; 30% inference latency reduction) and a rigorous evaluation/monitoring approach, while partnering closely with nurses and operations teams to shape workflows and guardrails.”

“Backend Python engineer focused on production-grade automation and reliability, with hands-on experience designing scalable API systems on PostgreSQL and making pragmatic architecture calls (modular monolith over premature microservices). Demonstrated measurable performance wins (50–60% latency reduction) and strong operational rigor via observability, incremental rollouts/feature flags, and security patterns like JWT + RBAC + database row-level security.”