Vetted Data Engineering Professionals

“Current CTO of an analytics company in the eye industry that was acquired by a larger organization, and now exploring building a new analytics platform for individual provider offices. Focused on helping practices grow revenue and improve the patient journey to drive long-term retention, with entrepreneurial motivation tied to building a legacy business for their family.”

“Technical Product Manager with hands-on experience shipping and running live free-to-play games (including Cartoon Network BMX Champions) across web and mobile, with exposure to Roblox and some console. Focuses on data-driven live ops—daily rewards, streaks, push notifications, and limited-time events—paired with A/B testing and funnel/retention analytics to improve engagement and IAP performance.”

“Startup builder (MagicSeven) who designed and implemented a browser-based, agentic video editor end-to-end, including an AWS event-driven multimodal LLM “indexing” pipeline and an orchestration LLM agent for searching and manipulating footage. Demonstrates deep video file/codec knowledge plus practical production hardening of LLM workflows (format validation, plan/execute, S3-based state for debuggability).”

Aldric Pinto

Screened ReferencesStrong rec.

Mid-level AI product and data lead specializing in analytics and healthcare AI

New Haven, CT4y exp

“Product-minded software engineering lead with a blend of backend, data engineering, cloud observability, and AI product experience. They’ve owned systems end-to-end, from ETL job builders that cut setup time 70% to hybrid-cloud observability workflows that reduced monitoring effort 80%, and also drove an AI marketing feature that improved conversion from 2% to 6%.”

“Former CTO at Enodo who led development of programmatic parsers to extract unstructured data from real-estate financial documents (rent rolls and T12s), validating with users via prototypes before productionizing. Emphasizes accuracy-driven engineering and scalable test-suite growth based on real user samples, and has experience scoping complex product ideas (e.g., browser-based narrative editor) down to an MVP.”

“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.”

“Backend/ML engineer who has shipped high-scale real-time systems across e-commerce and healthcare: built a PharmEasy real-time recommendation engine for ~2M monthly users (cut feature latency 5 min→30 sec; +15% cross-sell) and architected a HIPAA-compliant multimodal clinical diagnostic workflow (DICOM+EHR) with XAI, MLOps (MLflow/Airflow/K8s), and drift/monitoring guardrails supporting 10k+ daily predictions.”

“Repeat early-stage startup CTO/first engineer who helped take Vettery from 0 to a $100M+ exit. Led product-oriented engineering with heavy investment in data science/ML, including a recommendations system and candidate evaluation model (90%+ predictive effectiveness), and scaled the modeling stack using parallel processing and Apache Airflow.”

“Built a production real-time anomaly detection platform for high-frequency trading at HSBC, using a streaming stack (Pulsar + Spark Structured Streaming + AWS Lambda) and a transformer-based model combining time-series and numerical signals. Experienced in MLOps and safe deployment (Kubernetes, canary releases, MLflow/Grafana monitoring) and in aligning model performance with risk/compliance expectations through SLA-driven tuning and stakeholder-friendly dashboards.”

“Built and deployed a production RAG-based document Q&A system on Azure OpenAI to help business teams search thousands of PDFs/Word files, using Qdrant vector search, MongoDB, and a Flask API. Demonstrates strong production engineering (streaming large-file ingestion, parallel preprocessing, monitoring/retries) plus systematic prompt/embedding/chunking experimentation to improve accuracy and reduce hallucinations, and has hands-on orchestration experience with ADF/Airflow/Databricks/Synapse.”

“ML/NLP engineer who built a RAG-based technical assistant for Caterpillar field engineers, transforming PDF keyword search into intent-based semantic retrieval across manuals, logs, sensor reports, and technician notes. Strong in productionizing data/ML systems (Airflow, PySpark) with rigorous preprocessing, entity resolution, and evaluation—delivering measurable gains in accuracy, relevance, and duplicate reduction.”

“Built and deployed a production LLM system that scraped Google Maps menu photos, extracted structured prices via OpenAI, and cross-validated them against website-scraped data to automate data-quality verification at scale (replacing costly manual contractor checks). Demonstrates strong reliability instincts—precision-first prompting, output gating with image-quality metadata, and fuzzy matching/RAG techniques—plus solid orchestration (Dagster/Airflow) and observability (Sentry, Prometheus/Grafana).”