NO C2C - AgenticAI/ML Engineer (MLOps, Azure, RAG) - US Citizen or GC / GC EAD Only

AI/ML Engineer (3 openings) Due to the large amount of fraudulent candidates, all calls are monitored using software designed to detect the use of AI prompting tools or AI-assisted software. Duration: 6-12 month contract to permanent Location: 100% remote – CST working hours Role Summary We are seeking a hands-on AI / ML Engineer to design, build, and operate production-grade AI systems. This role goes beyond basic RAG or prompt engineering and requires ownership of the full AI lifecycle: from data and models to deployment, monitoring, and iteration. You will work on real-world, high-impact AI solutions in a fast-moving, engineering-driven environment. • This role is an AI/ML Engineer focused on GenAI, RAG, and Agentic AI, built Azure-first. Azure isn’t just the hosting environment; it’s the backbone of how the solution is designed and implemented. • The engineer will build custom RAG pipelines using Azure services like Blob Storage, Data Factory, and Azure Document Intelligence, with Python code handling chunking, embeddings, retrieval logic, and agent workflows. • This is a hands-on, client-facing role. The engineer runs meetings, demos solutions directly to the client, and operates in a fast-paced environment. This is not a prompt-engineering or research role. Required Skills • Building end-to-end GenAI systems: -Agentic AI, including RAG -Embeddings -LLM integration / LLM fine tuning The core of the role is building and operating end-to-end machine-learning systems in production • Proven experience designing and deploying AI systems end-to-end, not just POCs • Experience with vector databases and retrieval strategies • Exposure to data pipelines and orchestration (e.g., Azure Data Factory, Databricks) • Strong Python expertise with experience building production APIs (FastAPI - preferred, Flask, or equivalent) • Building AI solutions in Azure - Azure OpenAI / Cognitive Services Blob Storage, Data Factory, Databricks, Cloud Foundry. Must understand how to process PDFs • Machine Learning / MLOps - This team is training the models as part of the use case so this person will need some experience doing that (will need to know when NOT to use an LLM) • Model training, evaluation, and tuning • Feature engineering and data preparation • MLOps – Needs to understand retraining strategies and monitoring (drift detection, performance, error analysis) • Model versioning and lifecycle management • Excellent verbal communication – confident (not cocky) and comfortable being on video with stakeholders, running meetings, has the ability to explain AI outputs to business users who are not technical, address problems quickly, and able to work in a fast-paced environment (sometimes reaching finance teams can be difficult)