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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI Prototype Deployment Engineer - **Company:** Guidehouse Inc. - **Location:** McLean, VA, United States - **Salary:** $85,000.0 - $141,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Big Data, Cloud Engineering, Continuous Integration, Information Engineering, Data Security, Software Debugging, DevOps, Python (Programming Language), Performance Tuning, Systems Integration, Delivery Pipeline, Software Security, Pyspark, Deployment Automation, Machine Learning Operations, Software Version Control, Data Pipelines, Databricks - **Published:** August 18, 2026 - **Apply:** https://guidehouse.wd1.myworkdayjobs.com/External/job/US---VA-McLean/AI-Prototype-Deployment-Engineer_43642 ## About the Role * Experience working with Databricks, data pipelines, notebooks, and cloud-native deployment workflows. * Understanding of AI/ML lifecycle operations, including model packaging, testing, and validation. * Ability to work within remote or government-site environments as needed (Washington, DC or Northern Virginia). * Familiarity with deployment automation, DevOps practices, version control, and secure data-handling procedures. * Strong communication skills and ability to collaborate across technical and non-technical teams. What Would Be Nice To Have: * Experience supporting AI/ML programs within federal environments. * Familiarity with Advana, or similar large-scale data platforms. * Knowledge of CI/CD tools, model registries, Lakehouse architectures, and secure deployment pipelines. * Experience with Python, PySpark, or cloud orchestration technologies. ## Description This role provides technical support to prototype deployment activities for AI Studio efforts within the Advana ecosystem. You will assist in deploying, integrating, testing, and operationalizing AI prototypes that align with program needs and the work described in the RFP submission. All tracks will work within cloud-native environments, primarily Databricks, to ensure prototypes are production-ready, compliant, secure, and capable of supporting mission stakeholders. Responsibilities include coordinating with data engineering teams, validating model performance, ensuring deployment pipelines function reliably, and supporting documentation, configuration, and integration tasks. Responsibilities: AI Studio Prototype Deployment - Lead: * Lead cross-team deployment activities, ensuring alignment with overall program strategy and successful rollout of prototype capabilities. * Oversee deployment planning, resource coordination, and risk mitigation strategies. * Establish and enforce best practices for prototype integration, CI/CD patterns, security compliance, and operational readiness. * Mentor team members and serve as the primary point of contact for deployment-related progress and issues. AI Studio Prototype Deployment - Engineer: * Deploy AI prototypes using cloud-native tools, including Databricks and Advana operational environments. * Integrate prototype components with existing systems and data pipelines. * Support debugging, performance tuning, and deployment automation. * Work with cross-functional teams to validate prototype functionality and ensure readiness for stakeholders. AI Studio Prototype Deployment - Specialist: * Assist with preparing environments, running tests, and supporting deployment tasks within Databricks and associated cloud platforms. * Conduct initial quality checks and gather feedback during prototype rollout. * Maintain documentation and track deployment configurations and procedures. ## Related Videos - [PySpark - Combining Machine Learning & Big Data](https://www.wearedevelopers.com/videos/44-pyspark-combining-machine-learning-big-data) - [Alibaba Big Data and Machine Learning Technology](https://www.wearedevelopers.com/videos/37-alibaba-big-data-and-machine-learning-technology) - [Navigating the AI Wave in DevOps](https://www.wearedevelopers.com/videos/853-navigating-the-ai-wave-in-devops) - [From DevOps to Scaled DevOps: How We’re Rebuilding Continuous Delivery as a Platform](https://www.wearedevelopers.com/videos/100018-from-devops-to-scaled-devops-how-we-re-rebuilding-continuous-delivery-as-a-platform) - [Agentic DevOps: How AI-Powered Automation Transforms Software Delivery on GitHub and Azure](https://www.wearedevelopers.com/videos/1539-agentic-devops-how-ai-powered-automation-transforms-software-delivery-on-github-and-azure) - [Empowering Retail Through Applied Machine Learning](https://www.wearedevelopers.com/videos/976-empowering-retail-through-applied-machine-learning) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [From Prototype to Production: Build AI Agents with This Free 4-Course Learning Path](https://www.wearedevelopers.com/magazine/655-from-prototype-to-production-build-ai-agents-with-this-free-4-course-learning-path) - [Got AI ideas but no money? Here are 10 free ways to level up your AI skills with Google Cloud](https://www.wearedevelopers.com/magazine/600-got-ai-ideas-but-no-money-here-are-10-free-ways-to-level-up-your-ai-skills-with-google-cloud) - [What Industries Outside of AI Are Hiring The Most AI Experts?](https://www.wearedevelopers.com/magazine/98-what-industries-outside-of-ai-are-hiring-the-most-ai-experts) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development)