AI/ML Delivery Engineer

Type of Requisition:RegularClearance Level Must Currently Possess:NoneClearance Level Must Be Able to Obtain:NonePublic Trust/Other Required:NACLC (T3)Job Family:Data Science and Data EngineeringJob Qualifications:Skills:Artificial Intelligence (AI), End-to-End Testing, Machine Learning (ML)Certifications:NoneExperience:5 + years of related experienceUS Citizenship Required:NoJob Description:The AI/ML Delivery Engineer is a hands-on senior practitioner who designs, builds, and scales enterprise AI, ML, and data products across cloud environments. This role combines deep engineering expertise with solution architecture, delivery leadership, and executive-level communication. This role supports the NIH mission by delivering secure, scalable, and innovative AI and data solutions that accelerate biomedical research, improve operational efficiency, and enable data-driven decision-making to advance human health. MEANINGFUL WORK AND PERSONAL IMPACT The ideal candidate can move from strategy to production implementation across intelligent search, generative AI chat, agentic workflows, predictive ML, computer vision, and medical AI imaging. They bridge data science, software engineering, platform engineering, and security to deliver robust, governed, cost-aware, fit-for-mission AI solutions. Solution Design and Delivery Architect and deliver end-to-end AI/ML and generative AI solutions across the full lifecycle, including data integration, feature engineering, model development, evaluation, deployment, monitoring, and continuous improvement. Design scalable, secure, and governed AI/ML architectures that support enterprise data management, cloud-native services, distributed computing, and high-performance workloads. Develop and deploy production-ready AI solutions using modern techniques for large language models, intelligent search, workflow automation, model evaluation, monitoring, and continuous optimization. Integrate enterprise AI and cloud services with organizational data platforms, security controls, governance frameworks, and operational processes. Deliver AI and machine learning solutions across a variety of domains, including natural language processing, computer vision, predictive analytics, intelligent search, multimodal AI, and other mission-focused applications, using industry-standard frameworks and tools. Design secure application programming interfaces (APIs), integration services, data pipelines, and orchestration workflows that enable AI capabilities to be reliably consumed by enterprise systems and end users. Technical Leadership, Governance, and Delivery Excellence Serve as the AI/ML technical authority for cross-functional data science, engineering, platform, security, infrastructure, and business teams. Lead architecture reviews, AI readiness assessments, performance benchmarking, and infrastructure trade-off analysis for large-scale cloud, GPU, and distributed data workloads. Establish MLOps, LLMOps, and ModelOps practices for CI/CD, experiment tracking, model registry, prompt/model versioning, automated testing, deployment promotion, rollback, drift/quality monitoring, lineage, and cost optimization. Define responsible AI controls for privacy, IAM, key management, audit logging, PHI/PII handling, explain ability, model cards, bias/risk assessment, human oversight, and agentic guardrails. Mentor engineers and data scientists while communicating complex AI concepts through clear solution narratives, architecture diagrams, demonstrations, and executive-ready materials. Support solutioning activities by shaping technical approaches, estimating delivery patterns, and contributing to client responses. WHAT YOU’LL NEED TO SUCCEED Bring your expertise and drive for innovation to GDIT. The AI Engineer Sr must have: Education: Bachelors in Computer Science, Information Technology, Engineering, Mathematics/Statistics, Bioinformatics, Data Science, or equivalent professional experience. Experience: 5+ years of related experience Hands-on experience with modern enterprise data platforms and cloud-native data architectures, including distributed data processing, data governance, machine learning lifecycle management, and batch and streaming data pipelines. Production experience developing, deploying, and supporting AI/ML and generative AI solutions, including intelligent search, large language model (LLM) applications, workflow automation, model serving, evaluation, monitoring, and continuous improvement. Experience with modern machine learning and deep learning frameworks, libraries, and tools for developing, training, evaluating, and deploying AI/ML solutions. Experience designing, deploying, and managing AI/ML solutions in one or more major cloud environments, including cloud-native AI services, identity and access management, networking, security, and infrastructure. Experience working with large, complex, sensitive, or regulated datasets, including data integration, migration, quality manageme