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Vetted Model Monitoring Professionals
Pre-screened and vetted.
Mid-level AI/ML Engineer specializing in Generative AI and MLOps for financial services
Mid-level Machine Learning Engineer specializing in recommender systems and MLOps
Senior AI & Machine Learning Engineer specializing in Computer Vision, LLMs, and RAG
Mid-level Machine Learning & GenAI Engineer specializing in RAG and multimodal AI systems
Mid-level Data Scientist specializing in ML, MLOps, and Generative AI
Senior AI Engineer specializing in Generative AI and cloud-native ML platforms
Mid-level AI/ML Data Engineer specializing in MLOps and Generative AI
Mid-level Data Scientist / AI/ML Engineer specializing in MLOps, geospatial analytics, and GenAI
Mid-level Generative AI/ML Engineer specializing in LLMs, RAG, and MLOps
Mid-level MLOps/Machine Learning Engineer specializing in cloud-native production ML
Mid-level Machine Learning Engineer specializing in Generative AI and LLMOps
Mid-level AI/ML Engineer specializing in LLMs, RAG, and scalable model deployment
Junior AI/ML Engineer specializing in Generative AI production systems
Intern AI/ML Engineer specializing in NLP, graph analytics, and agentic RAG systems
Mid-level AI/ML Engineer specializing in risk modeling, healthcare analytics, and MLOps
Rukmini Pisipati
Junior AI/ML Engineer specializing in LLM automation and NLP
“Built and shipped a production LLM hallucination detection and monitoring pipeline using semantic-level entropy (embedding-clustered multi-generation variance) to flag unreliable outputs in downstream automation. Implemented a scalable async architecture (FastAPI + Docker + Redis/Celery) with strong observability (structured logs + PostgreSQL) and developed evaluation loops combining controlled prompts and human review; also partnered with non-technical stakeholders on AI-driven form validation/document processing.”
Vishesh Patel
Junior AI/ML Engineer specializing in Python ML, NLP, and model deployment
“Built and productionized a real-time social-media sentiment analysis system used by a marketing team to monitor brand/campaign performance. Experienced in orchestrating LLM workflows with LangChain (validation → prompting → parsing → post-processing), plus monitoring, retraining, and RAG-style retrieval using embeddings/vector stores to keep outputs reliable over time.”
Tirth Shah
Mid-level AI/ML Engineer specializing in anomaly detection, data tooling, and cloud-native systems
“Backend/platform engineer who built an LLM-driven QA automation system (“mockmouse”) using a Flask orchestration microservice, Socket.IO real-time updates, Redis caching, and strict Pydantic schemas to turn prompts into reliable action graphs and automated browser tests. Has hands-on Kubernetes delivery experience (Docker/Helm/Jenkins) and has supported large migration programs, validating ETL cutovers with 1M+ synthetic records and rigorous output comparisons; also built event-driven monitoring/anomaly detection streaming into Grafana.”