Vetted Machine Learning Professionals

“ML engineer with General Motors experience deploying production AI systems, including a BERT-based sentiment classifier for over a million customer support call transcripts (reported ~91% precision) and sub-200ms latency via FastAPI/Docker optimization. Also built predictive maintenance models and automated retraining/monitoring workflows using Airflow and MLflow, collaborating closely with non-technical customer support stakeholders.”

“Data professional with ~4 years of experience, most recently at AIG (insurance), building ML/NLP systems for fraud detection and policy automation using transformers, CNNs, and clustering/anomaly detection. Also developed a RAG-based knowledge retrieval system, iterating across embedding models and moving to production based on precision and latency SLAs, then containerizing and deploying with SageMaker and CI/CD.”

“Application/security-focused customer-facing implementer who has secured multi-customer data aggregation apps using per-tenant isolation, short-lived/scoped tokens, and vault-based secrets management. Troubleshoots production issues via API gateway logs and performance tuning, and runs repeatable onboarding playbooks with strong customer-specific and cross-project documentation. Emphasizes AWS least-privilege IAM and secure agent deployment patterns, plus container scanning practices that catch vulnerabilities pre-production and build developer trust.”

“Full-stack TypeScript/Angular/Node engineer who owned a production healthcare application for a pharmaceutical client, supporting 100K+ monthly users across 10+ countries. Strong focus on maintainability and quality (reusable localized component library, ~90% unit test coverage, SonarQube in CI/CD) plus performance work (reported 15% client-side latency reduction and up to 50% backend latency reduction) while migrating legacy mobile code with strict backward compatibility.”

“Data engineer with banking FP&A experience who led an end-to-end migration of 10+ TB from Teradata to Azure (ADF + Data Lake + Databricks/PySpark + Synapse). Emphasizes reliability (multi-stage validation, monitoring/alerts) and performance (Spark tuning, incremental loads, autoscaling), reporting ~99.5% pipeline reliability while supporting downstream consumers with stable schemas and clear change management.”

“Data/analytics engineer focused on finance and e-commerce integrations, building end-to-end pipelines and services across Odoo, QuickBooks, Snowflake, and Tableau. Replaced a costly third-party Walmart connector with a serverless AWS Lambda pipeline deployed via Terraform/GitHub and monitored with CloudWatch/Datadog, and shipped a bi-directional Odoo↔QuickBooks invoice sync with distributed locking plus Slack-based finance approvals.”

“Entrepreneurial product builder who created chaintrigger.com in response to early Web3 demand for real-time on-chain event reactions, offering an alternative to The Graph Protocol and achieving adoption among games and other Web3 projects. Currently developing new tools, dogfooding internally, and building distribution via personal network and a niche Twitter/X following to gather feedback and iterate quickly.”

“Product-focused candidate with live B2C/product-led growth experience who translates free-to-play game mechanics (onboarding as a core loop, progressive unlocking, nudges, variable rewards) into real-world web/mobile products. Strong in live experimentation, KPI-driven iteration, and monetization/retention measurement (DAU/MAU, funnels, LTV-focused A/B testing).”

“Founder building AdaptiveWorX™ Flow, a graph-native, agent-first operating system intended to replace document-driven/Agile-era workflows with continuously verifiable, causally linked delivery. Brings decades of systems/architecture perspective plus prior digital health experience, including a PoC turning behavioral therapy sessions into structured longitudinal data using real-time A/V capture and multimodal AI, with an emphasis on compliance, traceability, and scalable platform design.”