Vetted Predictive Modeling Professionals

“AI Engineering Intern at The Etherloop building the backend for a healthcare lifestyle recommendation app, including a multi-agent RAG-based system that uses curated SME data plus web search to generate personalized supplement recommendations from user lifestyle details and blood biomarkers. Evaluates against 500+ SME ground-truth profiles with ranking metrics and focuses on HIPAA-aligned deployment, privacy/security, and guardrails to reduce hallucinations and unsafe outputs.”

“Data/ML engineer in financial services (Northern Trust) who built a production RAG-based LLM system to connect structured transaction/portfolio data with unstructured market and internal documents for risk teams. Strong in end-to-end pipelines (AWS Glue/Airflow/PySpark), entity resolution, and taking models from prototype to reliable daily production with performance tuning (LoRA + TensorRT) and monitoring.”

“Built a 24/7 Python/LangChain email agent in production with validation, circuit breakers, human-review escalation, and structured observability. Also applied data and automation skills at Community Dreams Foundation, including turning a vague donor-insights request into a usable donor-risk prediction workflow and raising ETL reliability from roughly 85% to 99% by diagnosing SQLite concurrency issues.”

“Recent McCombs School of Business (UT Austin) Post Graduate Program graduate in Data Science & Business Analytics with hands-on project experience spanning stock clustering/segmentation and hotel booking-cancellation prediction. Strong in end-to-end analysis workflows (EDA, cleaning, feature engineering) and rigorous model comparison/selection, with exposure to boosting methods and imbalanced-data techniques; limited experience so far with embeddings/vector databases and production deployment.”

Sudheer koki

Screened ReferencesStrong rec.

Mid-level AI/ML Engineer specializing in predictive modeling, data pipelines, and RAG systems

Florida, USA5y exp

“Built and productionized an LLM-powered internal knowledge search system in a regulated environment, using embeddings/vector DB retrieval with strict grounding and confidence gating to reduce hallucinations. Reported ~45% accuracy improvement over keyword search and implemented end-to-end orchestration, monitoring, CI/CD, and incremental re-indexing to manage latency and data freshness while driving adoption with business stakeholders.”

“Backend-leaning full-stack engineer who has built AI-powered analytics products from 0→1, including a predictive analytics dashboard and an AI orchestrator for natural-language-to-database querying. Particularly strong in making LLM systems production-safe through schema validation, self-healing retries, monitoring, and retrieval optimization, with quantified impact on cost, latency, and quality.”