Vetted Machine Learning Professionals

“Built and operates a production LLM agent for enterprise IT operations that triages and drafts resolutions for high-volume ServiceNow tickets using LangChain + RAG (Pinecone/pgvector) and AWS Bedrock/OpenAI. Emphasizes reliability with schema-validated stages, offline eval datasets from real tickets, and CloudWatch-driven monitoring/guardrails; system scales to 40K+ tickets/month and cut resolution time ~28%.”

“Robotics/remote-sensing software engineer who built and validated multisensor image-processing and spectral chemical-detection pipelines (RX anomaly detection, ACE), including calibration protocols with a motorized shutter and rigorous data QC. Uses white-box NumPy simulators to debug SLAM/registration issues before translating logic to C++, and partnered with hardware teams to solve temperature-driven signal variation via combined software calibration and improved thermal management.”

Trevor Peace

Screened ReferencesStrong rec.

Senior Full-Stack Engineer specializing in Healthcare IT

San Francisco, CA27y exp

“Founding/sole engineer who has built multiple healthcare products from scratch, spanning medical billing, cardiology triage, and cardiac device monitoring. Particularly compelling for healthtech and AI roles: they combine full-stack TypeScript/AWS execution with strong product judgment in regulated settings, including shipping voice-enabled RAG workflows and rejecting unsafe AI automation when simpler human-in-the-loop UX was the right answer.”

“Full-stack product engineer who owned a live-events digital ticketing platform end-to-end, including blockchain-based ticket validation and high-traffic booking flows. Stands out for combining Angular/React frontend work with Java/Spring Boot backend architecture, plus strong production reliability practices around concurrency control, queues, observability, and UX optimization.”

“Built and productionized an LLM-driven document verification workflow for a construction firm’s submittals process, moving from a Vercel/Next.js prototype to a FastAPI + LangChain/LangGraph backend with background workers and multi-server deployment. Uses LLM tools (e.g., OpenAI Codex/Cloud Code) for rapid development and log-driven root cause analysis, and partners with customer teams on evaluation metrics and iterative improvements.”

Bibhash Biswas

Screened ReferencesStrong rec.

Executive technology leader specializing in AI/ML, SaaS, and cloud architecture

Los Angeles, CA25y exp

“Hands-on CTO and player-coach engineering leader from MindShow who led a 10-person team, transformed a services business into a licensable product sold to Fortune 500 customers, and stayed deep in architecture, AI, and UI work. Particularly notable for combining microservices/system design leadership with practical AI product delivery in speech and media workflows, including a phoneme-generation system the candidate says achieved 99.9%+ accuracy.”

“AI/ML Engineer (TCS) who built and deployed a production LLM-powered audit transaction validation service to reduce manual review of unstructured transaction records and comments. Implemented a LangChain/Python pipeline for extraction/normalization and discrepancy detection, with strong production reliability practices (decision logging, dashboards, labeled eval sets) and a human-in-the-loop auditor feedback loop to improve precision/recall under strict data-sensitivity and near-real-time constraints.”

“AI engineer who built a production e-commerce system that analyzes product images alongside sales and demographic data to generate actionable creative recommendations, now used by 20+ clients. Also built orchestrated document/agent pipelines (Airflow, LangGraph) including a compliance drift detector auditing 401 compliance documents, with an emphasis on traceability, logging, and production integration.”

Mandy Anger

Screened ReferencesStrong rec.

Executive product leader specializing in EdTech and AI-powered learning products

Idaho Falls, ID16y exp

“Education-focused product leader with a career spanning Waterford, WGU, ETS, Stukent, and Accelo, combining zero-to-one AI product building with portfolio-scale operating rigor. Particularly compelling for mission-driven edtech roles: they launched an AI-powered K-12 product line with five state agency partnerships in under a year and consistently frame AI as a tool to amplify, not replace, educator and tutor judgment.”

“AI product leader with experience at Bloomberg Tax and Sun Automation, focused on turning complex enterprise workflows into approachable, human-in-the-loop AI experiences. Notable for redesigning a technical data integration product around natural language and guided validation, driving a 72% reduction in onboarding/adoption time while emphasizing trust, transparency, and user control.”

“Product and design leader who has built 0-to-1 products across proptech and healthtech, including founding GoodFences and creating an explainable AI architectural review engine for HOAs. Combines deep user research, strong AI product judgment, and team-building experience, with a personal connection to educational equity as an immigrant and parent in the public school system.”

“Internship experience building an end-to-end medical AI pipeline that extracts and normalizes messy medical PDFs, fine-tunes BioBERT to classify tumor-related statements (including negation/ambiguity handling), and integrates image-model outputs (MedSAM/GroundingDINO) for tumor localization and classification. Also worked on an LLM/RAG system to draft IPO prospectuses using retrieved regulatory/financial sources (including SEC EDGAR) with structured prompts to reduce hallucinations.”

“Interned at Larsen & Toubro where they built and deployed an agentic RAG document question-answering system to reduce time spent searching documents and improve trustworthiness. Implemented ReAct-style multi-step orchestration with LangChain/LlamaIndex plus evidence-bounded generation, grounding/citations, and rigorous evaluation—cutting latency ~40%, hallucinations ~35%, and unsafe outputs ~40% while collaborating closely with non-technical business/ops stakeholders.”