Vetted Model Monitoring Professionals

Vamsi Reddy

Screened

Mid-level AI/ML Engineer specializing in healthcare and financial ML systems

Nashville, TN5y exp

“ML/AI engineer with hands-on experience shipping both predictive healthcare models and clinical GenAI assistants into production. They combine strong MLOps depth across Azure and AWS with healthcare-specific safety thinking, including PHI guardrails, retrieval grounding, and production monitoring, and they also built internal Python tooling for fraud ML workflows at Capital One.”

“ML/AI engineer with strong production depth across classical ML, MLOps, LLM/RAG, and scalable Python data platforms, with experience at Cisco and Accenture. Stands out for tying technical decisions to measurable business outcomes, including $1.2M annual savings, 40% faster support resolution, and broad internal adoption of shared engineering frameworks.”

“AI/ML engineer with production experience across both classical ML and Generative AI, including a real-time banking fraud detection platform at Deloitte and a RAG-based cybersecurity threat analysis feature at Accenture. Stands out for owning systems end-to-end—from feature pipelines and model tuning through deployment, monitoring, retraining, and API/platform reliability—with measurable impact on fraud accuracy, false positives, and SOC analyst efficiency.”

“AI engineer and current tech lead building a RAG-based multi-agent QA platform for financial document analysis at significant scale (40,000-50,000 documents). They combine Python, CrewAI, FastAPI, Hugging Face embeddings, Pinecone, and AWS SageMaker to deliver retrieval, calculation, summarization, forecasting, and visualization workflows, while leading a small cross-functional team.”

“Frontend-focused engineer with experience spanning healthcare, enterprise analytics, and real-time trading products. They have owned React/TypeScript dashboard surfaces end-to-end, including a hospital patient dashboard that cut latency by 50%, and have also shaped backend WebSocket contracts to make real-time systems scale.”