AI/ML Lead Software Engineer

AI/ML Lead Software Engineer Location: USA DC Washington 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 Job Qualifications: Skills: 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 learning. Experience monitoring deployed models including drift detection, performance tracking, and setting retraining triggers. Experience with at least one major cloud platform such as Azure, AWS, or GCP for data and AI workloads. Familiarity with Docker and Git. Skill in data visualization using Power BI or Tableau. Strong system analysis skills to identify viable AI insertion points in business processes, products, or workflows. Ability to clearly communicate technical findings and translate them into business value. Ability to document models, assumptions, data lineage, and decisions. Awareness of Responsible AI principles including fairness, explainability, privacy, and compliance considerations. Basic understanding of data security practices and access controls in production environments. Preferred Skills Experience with large language models such as Azure OpenAI Service or the OpenAI API for summarization, classification, or copilots. Experience with prompt engineering and evaluating LLM outputs for quality and safety. Experience with RAG pipelines and vector databases such as Azure AI Search, Pinecone, or FAISS. Knowledge of fine‑tuning and adaptation strategies for domain-specific use cases. Experience with model orchestration and experiment tracking tools such as MLflow or Weights & Biases. Experience with Kubernetes and ML deployment tools such as AKS, EKS, Argo, or KServe. Experience with feature stores, A/B testing frameworks, and event-driven or streaming services such as Kafka or Kinesis. Experience with CI/CD tools such as GitHub Actions or Azure DevOps and with IaC tools such as Terraform or Bicep. Experience with Databricks, Snowflake, or BigQuery. Experience building robust APIs such as REST or GraphQL and microservices supporting machine learning workloads. Knowledge of monitoring and observability technologies such as Prometheus, Grafana, and associated logs. Experience with Responsible AI and compliance practices including explainability tools such as SHAP and LIME and model risk management. Knowledge of privacy-by-design and PII handling practices including minimization and anonymization. Familiarity with FedRAMP or other regulated environments if applicable. Experience using R, PySpark, or Scala for large-scale data workloads. Experience with LangChain or Semantic Kernel for developing LLM applications. Advanced Tableau or Power BI experience including parameterized dashboards and row-level security. Ability to support a 24x7 environment for business-critical or SLA-driven workloads. Location: This is a hybrid role that requires 3 days per week at the client site in Southwest Washington DC. Visa Sponsorship Will Not Be Provided for This Position Clearance: Candidates must be eligible to obtain a federal security clearance GDIT IS YOUR PLACE: Full-flex work week to own your priorities at work and at home 401K with company match Comprehensive health and wellness packages Internal mobility team dedicated to helping you own your career Professional growth opportunities including paid education and certifications Cutting-edge technology you can learn from Rest and recharge with paid vacation and holidays The likely salary range for this position is $128,039 - $173,229. This is not, however, a guarantee of compensation or salary. Rather, salary will be set based on experience, geographic location and possibly contractual requirements and could fall outside of this range. Scheduled Weekly Hours: 40 Travel Required: None Telecommuting Options: Hybrid Work Location: USA DC Washington Additional Work Locations: Total Rewards at GDIT: Our benefits package for all US-based employees includes a variety of medical plan options, some with Health Savings Accounts, dental plan options, a vision plan, and a 401(k) plan offering the ability to contribute both pre and post-tax dollars up to the IRS annual limits and receive a company match. To encourage work/life balance, GDIT offers employees full flex work weeks where possible and a variety of paid time off plans, including vacation, sick and personal time, holidays, paid parental, military, bereavement and jury duty leave. To ensure our employees are able to protect their income, other offerings such as short and long-term disability benefits, life, accidental death and dismemberment, personal accident, critical illness and business travel and accident insurance are provided or available. We regularly review our Total Rewards package to ensure our offerings are competitive and reflect what our employees have told us they value most. We are GDIT. A global technology and professional services company that delivers consulting, technology and mission services to every major agency across the U.S. government, defense and intelligence community. Our 30,000 experts extract the power of technology to create immediate value and deliver solutions at the edge of innovation. We operate across 50 countries worldwide, offering leading capabilities in digital modernization, AI/ML, Cloud, Cyber and application development. Together with our clients, we strive to create a safer, smarter world by harnessing the power of deep expertise and advanced technology. Join our Talent Community to stay up to date on our career opportunities and events at gdit.com/tc. Equal Opportunity Employer / Individuals with Disabilities / Protected Veterans