AI/ML Lead Software Engineer

Job Description: Type of Requisition: Regular Clearance Level Must Currently Possess: None Clearance Level Must Be Able To Obtain: None Public Trust/Other Required: MBI (T2) Job Family: Software Engineering Skills: Job Qualifications: Artificial Intelligence (AI), Machine Learning (ML), Software Engineering, Technical Leadership Certifications: None Experience: 8 + years of related experience US Citizenship Required: No Job Description: At GDIT, we deliver clarity with our cloud, AI, and data-driven solutions—and we provide work that makes a real impact. Your expertise will help modernize mission-critical systems and accelerate innovation for our federal clients. We are seeking an experienced AI/ML Lead Software Engineer responsible for designing, developing, and implementing advanced machine learning models and artificial intelligence solutions to solve complex problems, optimize processes, and enhance decision-making. This role works closely with data scientists and software engineers to build scalable, efficient systems powered by advanced algorithms and large datasets. If you excel at architecting AI/ML solutions, integrating with enterprise platforms, and delivering production-ready models, this role offers the opportunity to drive significant technical impact. HOW AN AI/ML SOFTWARE LEAD ENGINEER WILL MAKE AN IMPACT: Design, develop, implement, and use machine learning algorithms and models to address business challenges and opportunities such as predictive analytics, natural language processing, computer vision, and recommendation systems. Collect, clean, and preprocess large volumes of structured and unstructured data from various sources, ensuring data quality, integrity, and relevance for model training and evaluation. Train, validate, and optimize machine learning models using state-of-the-art techniques and frameworks. Evaluate model performance, interpret results, and iterate on model design as needed. Extract, select, and engineer relevant features from raw data to improve model performance and generalization capabilities. Utilize domain knowledge and data exploration techniques to identify informative features. Deploy machine learning models into production environments and integrate them with existing systems and applications. Implement scalable, efficient, and reliable solutions for real-time or batch inference. Monitor model performance, reliability, and scalability in production environments. Implement automated monitoring and alerting systems to detect anomalies and performance degradation. Document technical designs, implementation details, and best practices for AI solutions. Collaborate with cross-functional teams including data scientists, software engineers, product managers, and stakeholders to understand requirements, prioritize projects, and deliver impactful AI solutions. Perform additional duties as assigned. Coach and provide guidance to less experienced professionals as required. Serve as a team or task lead if needed. Work independently under general supervision. WHAT YOU’LL NEED TO SUCCEED: Required Education / Skills Bachelor’s degree in a relevant field and 8+ years of experience. Hands-on experience with the Alteryx data blending platform. Strong Python skills including data manipulation, model development, and experience with libraries such as Pandas, NumPy, and scikit-learn. SQL proficiency including joins, window functions, and performance-optimized queries. Knowledge of statistical foundations such as probability, hypothesis testing, regression, and experimental design or A/B testing. End-to-end machine learning workflow experience including feature engineering, training, validation, deployment, and monitoring. Experience with data wrangling, ETL and ELT, and building reliable data pipelines capable of handling large and messy datasets. Experience with model evaluation including metrics selection, bias and variance analysis, and error analysis. Ability to integrate AI solutions with MLOps workflows. Experience integrating APIs for AI services such as model endpoints and microservices. Experience deploying models in production environments including packaging, versioning, and CI/CD for machine l