Vetted Machine Learning Professionals

“Capgemini engineer with 4+ years building and deploying high-availability, low-latency fraud detection APIs and multi-cluster distributed systems for a Fortune 20 bank, including zero-downtime production rollouts and multi-layer (SQL/network/hardware) performance debugging. Also built a Python + OpenAI/LangChain LLM-powered grading workflow for Austin School for Women, cutting feedback time from 90 minutes to 5 minutes per submission for 200+ learners.”

“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.”

“Senior engineering manager who stays hands-on (~50/50) while leading teams through design reviews, code reviews, and production issue triage. Shipped scalable platform features (notifications, user action tracking via microservices) with strong quality/performance practices (TDD, high-load testing). Owned a complex cross-system SSO/IdP incident end-to-end, identifying a SameSite+iFrame root cause and delivering a configurable product fix plus support documentation.”

“Cloud engineer and cloud OS SME (Chevron) who productionized large-scale security remediation—using Tanium and Ansible to address CIS benchmark noncompliance across 5,000+ servers with robust logging and RCA handoffs. Also drives adoption of a geospatial AI refinery inspection product by consolidating siloed imagery into an enterprise geospatial database, and presents internally on agentic/LLM tooling (LangChain/LangGraph, LangSmith observability).”

“Software engineer with experience at Wipro and Tylmen Tech owning customer-facing onboarding and real-time features end-to-end (React + Spring Boot) and building TypeScript/React apps backed by Node.js microservices (MongoDB, RabbitMQ). Strong in production reliability and fast iteration: feature-flagged rollouts, idempotent APIs/consumers, DLQs, and SLO-driven incident tooling, including an internal QA & release dashboard adopted by engineering and support teams.”

“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.”

“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.”