AI/ML Engineer (Active Secret) — Applied AI & Automation

About this role

AI/ML Engineer — Applied AI & Intelligent Automation Remote Active Secret Clearance Required Build AI That Moves Into Production Rackner is seeking an AI/ML Engineer to help shape how artificial intelligence is evaluated, built, governed, and deployed across a federal data modernization environment. This role goes beyond isolated prototypes or model development. You will help determine where AI can create meaningful operational value, whether AI is the right solution, and how promising use cases move from concept into production.

You will have the opportunity to build AI applications, agents, intelligent workflows, and automation while also influencing how emerging AI and LLM technologies are evaluated, governed, monitored, and adopted. Your work may span use-case assessment, solution design, prototyping, testing, deployment, responsible-AI controls, and post-production monitoring. Working alongside engineering, data, cybersecurity, governance, and mission teams, you will gain visibility across the full AI delivery lifecycle and help shape both the technology and the decisions behind it.

For an engineer who wants to expand beyond a narrow model-development role, this position offers exposure across: Applied AI engineering → AI/LLM evaluation → intelligent automation → responsible AI → enterprise adoption You will help turn emerging AI capabilities into practical tools that support real operational needs—not innovation for innovation’s sake. Then I’d go directly into: What You’ll Own Turn operational challenges into practical AI use cases, technical requirements, and solution designs Build and deploy AI applications, agents, intelligent workflows, and process automations Take solutions from intake and prototype through testing, production deployment, and monitoring Apply Python to AI/ML prototypes, integrations, evaluations, and automation Connect AI capabilities with enterprise applications, APIs, workflows, and data platforms Create intelligent document workflows for classification, extraction, summarization, routing, and related use cases Evaluate AI, generative AI, and LLM platforms for quality, security, reliability, scalability, risk, and organizational fit Design pilots, evaluation criteria, test plans, success measures, and adoption recommendations Shape responsible-AI controls covering model evaluation, performance monitoring, human oversight, risk, and incident response Apply tools such as Power Automate, Power Apps, Copilot Studio, or comparable intelligent-automation platforms Develop AI-driven analytics capabilities including predictive modeling, forecasting, and natural-language interaction with enterprise data Partner with stakeholders and technical teams to move useful AI capabilities from concept into sustainable operational use What You'll Bring A track record of building, integrating, evaluating, or deploying AI/ML capabilities in real organizational environments Strong Python skills Hands-on work with generative AI, LLM applications, machine learning, intelligent automation, or related applied-AI technologies Ability to integrate solutions with APIs, enterprise systems, workflows, or data sources Understanding of the AI delivery lifecycle from requirements and prototyping through testing, deployment, monitoring, and iteration Working knowledge of responsible-AI, governance, model evaluation, or AI risk-management practices Technical judgment to assess feasibility, value, implementation complexity, and risk Ability to translate operational needs into practical technical solutions Clear communication across both technical and nontechnical stakeholders Active Secret clearance Valuable Additional Background You do not need every item below to be successful in the role.