AI Infrastructure Engineer
10x national securityDenver (CO)
About the role
AI Engineer The AI Engineer will design, develop, and deploy scalable machine learning and AI-driven analytics capabilities. Responsibilities include multi-source data fusion, entity resolution and behavioral modeling, predictive and prescriptive intelligence analytics, and autonomous detection and alerting pipelines. You will operate across the full lifecycle from data ingestion to model deployment to operational feedback loops.
Core responsibilities include:
AI/ML engineering and model development, data engineering and pipeline integration, operational deployment (MLOps/DevSecOps), explainability and analyst integration, and collaboration and mission alignment. Required qualifications include active TS/SCI clearance, a bachelor's or master's in computer science, AI, data science, engineering, or related field, and 3–10+ years of experience in AI/ML engineering or applied data science. Technical expertise includes strong proficiency in Python, data frameworks, graph analytics, network analysis, anomaly detection, behavioral modeling, entity resolution, Kubernetes, Docker, microservices architectures, REST APIs, and distributed systems.
Before you apply
Applying takes about a minute. These four things decide how fast it moves after that.
Your profile is current
It's what we read first. Occupations, seniority and locations matter more than a long history.
Two examples you can talk through
Not a portfolio — just two pieces of work where you can explain the decisions and what you'd change.
A number in mind
What you're on now and what would make you move. We negotiate better when we know both.
Your notice period
Employers plan around it, and it's the question that stalls offers most often.
Once you apply, someone reads it and calls you before anything reaches the employer — usually within two working days.
More like this
