Vetted Model Fine-tuning Professionals

“Built a production AI-native internal onboarding feature that reduced manual product setup effort by combining barcode API data, product photos, structured LLM outputs, and a polished real-time camera UI. Demonstrates hands-on experience across the full stack of LLM systems: prompt/schema design, multimodal inputs, backend orchestration with SQS and vector retrieval, and production reliability through evals, telemetry, and drift monitoring.”

“ML/AI engineer with hands-on ownership of production recommendation and GenAI systems, spanning experimentation, deployment, monitoring, and iteration. Stands out for delivering measurable outcomes—22% CTR lift, 15% conversion lift, and a 30% reduction in support tickets—while demonstrating strong judgment on latency, cost, and safety tradeoffs in real-world systems.”

“AI/LLM engineer with healthcare domain experience who built a production clinical support “chart bot” for Molina, including PHI-safe ingestion of 200k+ PDF policies, vector retrieval, and a fine-tuned LLaMA served via vLLM on ECS Fargate. Demonstrated measurable performance wins (HNSW + namespace partitioning; 30% inference latency reduction) and a rigorous evaluation/monitoring approach, while partnering closely with nurses and operations teams to shape workflows and guardrails.”

“Built a production AI-powered sales automation system that reads inbound product enquiry emails, extracts structured data, and routes decisions via a rules-based workflow integrated with a product database. Leverages Gemini structured outputs/schema plus option-based prompting and validation to keep responses reliable, and optimizes latency by breaking agent reasoning into smaller LLM calls; evaluates workflows with LangSmith and metrics like completion rate and accuracy.”