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Vetted Feature Engineering Professionals
Pre-screened and vetted.
Mid-level AI/ML Engineer specializing in LLM agents, RAG pipelines, and AI automation
Junior Machine Learning Engineer specializing in healthcare and IT analytics
Mid-level Data Scientist specializing in ML, NLP, and Generative AI
Entry-Level Machine Learning Researcher specializing in HPC telemetry modeling
Intern Full-Stack & AI Engineer specializing in ML-driven mobile and data platforms
Mid-level AI/ML Research Engineer specializing in NLP, LLM agents, and multimodal systems
Mid-level AI/ML Engineer specializing in fraud detection, credit risk, and NLP/RAG
Mid-level AI/LLM Application Engineer specializing in RAG, agents, and Python/PyTorch
Junior AI/ML Engineer specializing in LLMs, automation, and backend data pipelines
Entry-level Machine Learning Engineer specializing in LLMs, RAG, and data pipelines
Mid-level Generative AI Engineer specializing in LLMs, RAG, and MLOps
Intern Full-Stack Software Engineer specializing in Node.js, AWS, and scalable backend systems
Entry-Level Full-Stack Engineer specializing in backend APIs and cloud architectures
Mid-level AI & Data Science professional specializing in MLOps, deep learning, and UAV research
Mid-level AI/ML Research Engineer specializing in NLP, LLM agents, and multimodal systems
Mid-level AI/ML Engineer specializing in GenAI, agentic AI, and RAG pipelines
Jeevan Kumar
Mid-level Full-Stack AI Engineer specializing in LLM systems and RAG
“Built and shipped a production "Campaign AI" multi-agent system (LangGraph) that personalizes B2B outbound emails at scale using Apollo.io prospect data, clustering-based segmentation, and 21 persona variants. Notably uncovered that high click rates were largely email security scanners and created a validated bot-detection/scoring pipeline (timestamps/IP/user-agent/click patterns), bringing reported engagement down from ~40% to a trusted 5–8% that aligned with real conversions.”
Mahaboob Johny
Mid-level Data Analyst specializing in SQL/Python analytics, ETL pipelines, and BI dashboards
“Data/AI practitioner who built a production LLM-driven healthcare claims analytics and dashboarding system to reduce avoidable ER visits—processing 1.4M+ claims, flagging 19% as non-emergent, and projecting ~$2.8M in annual savings. Demonstrates strong real-world LLM reliability and performance engineering (grounding, numeric validation, caching, materialized views, quantization) plus orchestration experience with Airflow and Azure Data Factory.”
Mid-level Generative AI & ML Engineer specializing in LLMs, RAG, and MLOps