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Vetted Model Monitoring Professionals
Pre-screened and vetted.
Mid-Level Full-Stack Software Engineer specializing in cloud-native security & compliance platforms
Mid-level Full-Stack/AI Engineer specializing in LLM microservices, RAG, and data pipelines
Mid-level Data Scientist specializing in GenAI, MLOps, and computer vision for robotics
Mid-Level Full-Stack Software Engineer specializing in healthcare web apps and LLM integrations
Mid-level Generative AI Engineer specializing in LLMs, RAG, and MLOps
Mid-level Generative AI Engineer specializing in LLMs, RAG, and prompt engineering
Mid-level AI/ML Engineer specializing in fraud detection, credit risk, and NLP
Senior Data Scientist / ML Engineer specializing in NLP, speech AI, and computer vision
Mid-level AI/ML Engineer specializing in Generative AI, NLP, and RAG systems
Mid-level AI & Data Science professional specializing in MLOps, deep learning, and UAV research
Mid-level Generative AI & ML Engineer specializing in LLMs, RAG, and MLOps
Jaiden Kettleson
Entry-Level Full-Stack & AI Engineer specializing in chatbots and web apps
“Data Science honors graduate (Maryville University) who has built Python/SQL backends and a capstone website handling sensitive user data. Emphasizes secure data handling (password encryption, secure database updates) and uses Git/GitHub Pages with CI/CD-style practices for managing and deploying changes.”
Iaroslav Kuznetsov
Mid-level Machine Learning Engineer specializing in AdTech and scalable data systems
“Built and scaled an internal AI code-search/assistant agent that expanded from engineering-only to broader internal users, tackling legacy code and inconsistent standards to make a RAG pipeline production-ready. Uses a metrics-driven approach (user feedback + automated Python evaluation for retrieval relevance and latency) and has handled high-pressure outages, including moving parts of the stack off AWS and adopting Milvus on internal infrastructure for resilience.”
Muhammad Murtaza Murtaza
Mid-level Machine Learning Engineer specializing in NLP, Computer Vision & Predictive Analytics
“Built a production LLM fine-tuning pipeline for domain-specific code generation at Pigeonbyte Technologies, including automated collection and rigorous quality filtering of 10M+ code samples (AST validation, sandbox execution/testing, deduplication, drift monitoring, and human-in-the-loop review). Also implemented end-to-end ML orchestration in Apache Airflow with data quality gates, dataset versioning in S3, benchmarking, and automated model promotion, and has a reliability-first approach to agent/workflow design.”
Aditya Reddy Gunda
Intern AI/ML & Data Engineer specializing in deep learning, NLP, and cloud data pipelines
“AI/ML practitioner with production experience building a RAG-powered contextual customer support agent, optimizing for low latency using vector databases and smaller LLMs. Also deployed a fraud detection model on Kubernetes with auto-scaling for heavy transactional loads, and improved chatbot accuracy by 15% through metric-driven testing and evaluation. Partners with Marketing on personalization/recommendation initiatives with measurable outcomes tied to customer feedback.”
Senior AI/ML Engineer specializing in NLP, LLMs, speech, and computer vision
Senior AI/ML Engineer specializing in NLP, LLMs, speech, and computer vision
Senior AI/ML Engineer specializing in NLP, LLMs, speech, and computer vision
Junior Machine Learning Engineer specializing in Agentic RAG and Document AI
“Built and shipped a production-grade RAG-powered news summarization and Q&A product, tackling real-world issues like retrieval drift, hallucinations, latency, and autoscaling deployment (Docker + FastAPI + Streamlit Cloud). Experienced in end-to-end ML/LLM workflow automation using Airflow, Kubeflow Pipelines, and MLflow, and has demonstrated business impact (40% inference precision improvement) through close collaboration with non-technical stakeholders at Evoastra Ventures.”
Akshay Amudhan
Entry Machine Learning Engineer specializing in quantitative finance and DeFi
“Built and deployed a production RAG chatbot using a vector database + LangChain-orchestrated pipeline, focusing on grounded, context-aware responses. Demonstrates practical trade-off thinking (retrieval quality vs latency/cost), hallucination control, and iterative improvement through logging, manual review, and stakeholder feedback loops.”