Vetted Data & Analytics Professionals

“Backend engineer who built and evolved Python/FastAPI services (including AWS-deployed ML prediction APIs) for real-time profitability and risk insights at TenXengage. Emphasizes pragmatic architecture, strong validation/observability, and secure access controls (RBAC + row-level filtering), and has led safe migrations via parallel runs and incremental rollouts; reports ~20% forecasting accuracy improvement.”

“LLM/AI engineer who has deployed production RAG conversational analytics and Text-to-SQL systems over Snowflake and curated data marts, emphasizing enterprise-grade guardrails for accuracy, security, and cost. Notable for a structured approach to reducing hallucinations (curated metric/table registry, SQL validation, RBAC, and citation-backed responses) and for building resilient, observable multi-step agent workflows using LangChain/LlamaIndex and Airflow.”

“Built a LangGraph-based, tool-routing LLM chatbot to deliver fast, trustworthy investment-stock insights (including tariff impact) and deployed it to production on Snowflake after initially developing in Azure with AI Search and the Microsoft Agent Framework. Improved routing robustness by moving from LLM-based decisions to a deterministic router backed by schema-relationship graphs and YAML metadata, and ran the project iteratively with non-technical stakeholders over an 8-month engagement.”

“LLM/agentic AI builder who deployed a production ITSM automation agent on Google ADK integrating ServiceNow and FreshService, with strong safety guardrails (human-approval gating and runbook-only command execution) and rigorous evaluation (500 synthetic tickets; 80%+ false-positive reduction). Also partnered with finance to deliver an AI agent that automated invoice/SOW retrieval and monthly reporting to account managers, reducing manual back-and-forth.”

“Backend/applied AI engineer from Cloud Rack Systems who built production GenAI/RAG and data platforms on Azure/Databricks at enterprise scale (2.5M records/day). Known for making LLM systems behave like deterministic services via strict retrieval contracts, citation-based validation, and strong observability—shipping a knowledge assistant used daily by 50+ users while driving hallucinations near zero and materially improving latency and cost.”

“Data-driven growth and partnerships professional with experience leading an analytics/reporting vendor rollout end-to-end (vendor selection via stakeholder interviews and PoC, then negotiating scope/pricing/support and tracking adoption/efficiency/accuracy KPIs). At PC Financial, built regression and segmentation models to optimize multi-channel targeting (in-app/email/push), driving +15% campaign engagement and +10% PC Optimum offer loads, and ran behavior-triggered lifecycle experiments that lifted upsell conversion by 20%.”

“Built and shipped a production AI/LLM agent-based, event-driven credit underwriting/decisioning workflow that automated document understanding, retrieval, risk scoring, and compliance checks—cutting turnaround from ~90 days to ~5 minutes while boosting throughput 200x+ and approvals ~50%. Experienced with Airflow/Prefect orchestration, Redis/RabbitMQ queues, rigorous eval/monitoring, and close collaboration with non-technical underwriting teams.”

“Data science professional who has built a production RAG-based LLM question-answering system ("Flash Query") to deliver fast, accurate answers over large document collections, focusing on retrieval quality and grounded responses. Also collaborates with non-technical retail/jewelry stakeholders to turn business questions into predictive models and dashboards for decision-making.”

“Operations- and growth-oriented candidate who improves external partner workflows through standardization and measurement (cut turnaround time ~40% while maintaining 99% accuracy). Also launched and scaled a university Excel/data analysis workshop using ICP-driven GTM and a tracked acquisition loop, increasing attendance 15% and generating 95% repeat-demand intent.”

“LLM/agent systems builder who deployed a production hybrid router for immerso.ai that dynamically selects retrieval vs reasoning vs generative pathways, achieving an 82% factual-accuracy lift. Deep hands-on experience optimizing local Mistral 7B inference (4–5 bit GGUF quantization, KV-cache reuse) and building reliable RAG/agent workflows with LangChain/LangGraph/AutoGen across GCP Cloud Run and AWS (ECS/Lambda).”

“Built and supported a production analytics backend (Python, PostgreSQL/Teradata, Airflow) powering KPI/reporting dashboards, and resolved peak-time latency/timeouts through systematic SQL tuning (EXPLAIN ANALYZE, indexing, query rewrites, pre-aggregations). Also shipped an applied AI-style feature that generates plain-language report summaries from pre-computed metrics with validation, monitoring, and fallback to manual review.”

“Frontend-focused builder who has led multiple projects end-to-end, including a React/Vite/TypeScript weather app and an internal analytics dashboard optimized for large, time-based datasets. Also created and shipped AetherGrid, a full-stack Windows desktop app, iterating with 5–10 testers and implementing pixel-perfect native UI details plus installer/uninstaller packaging; mentions starting a full-time role at Meta.”

“Console game testing experience focused on controller/input responsiveness (e.g., passing/shooting delays) with bug documentation shared to developers via notes/Excel. Also worked as an AI data tagging agent for an e-commerce LLM and built a Python + OpenAI API micro-tool to pre-label queries, improving labeling throughput by ~30% with a human-in-the-loop QA process.”