Mid-Level AI Engineer

Description Overview Join a team where innovation meets mission. Our AI, cloud, cyber, and modernization solutions save agencies thousands of hours, safeguard national security, and strengthen health and humanitarian missions worldwide. With 1,700+ team members, 1,500+ AI/data experts, and 100+ prime contracts, we deliver at scale and with purpose. We've been recognized as a Top Workplace by the Washington Post for six straight years and named to the Inc. 5000 Fastest Growing Private Companies 13 of the past 14 years. Credence is a welcoming home for those looking to grow and contribute to positive change. We encourage all employees to expand beyond their boundaries, dive into important world-changing Federal challenges. Position Summary Credence has an immediate need for a Mid-Level AI Engineer to join our growing AI and Automation practice. You will be a technical anchor in our AI and Automation practice. You'll apply foundational AI/ML skills to build and deploy data-driven solutions. Under mentorship from senior AI leaders, you'll drive model development lifecycles and collaborate across engineering, data, and stakeholder teams to deliver high-impact, cloud-native AI capabilities that advance federal missions. Responsibilities include, but are not limited to the duties listed below AI Model Development & Integration Support end-to-end AI development: data prep, feature engineering, model training, evaluation, and integration into production pipelines. Collaborative Engineering Work alongside data engineers, software engineers, and data scientists to embed AI into scalable systems and applications. Cloud & MLOps Enablement Help automate model deployment workflows using Infrastructure as Code (IaC), CI/CD pipelines, and container orchestration tools. Generative

AI & LLM

Usage Contribute to projects using generative AI and LLMs, helping to prototype, customize, and refine models. Production Monitoring & Optimization Monitor AI systems post-deployment, perform performance tuning, and apply best practices for reliability and scalability. Technical Rigor & Documentation Write clean, well-documented code following industry and federal guidelines; support reproducible development. Professional Growth Stay current on AI/ML trends and tools, and actively learn from senior team members through mentorship and technical design reviews.