AI/ML Engineer

Job QualificationsCascading Style Sheets (CSS), Data Science, Machine Learning (ML), Machine Learning AlgorithmsJob DescriptionThe AI/ML Engineer is responsible for designing, developing, and implementing machine learning models and artificial intelligence solutions to solve complex problems, optimize processes, and enhance decision‐making. They work closely with data scientists and software engineers to build scalable, efficient systems while leveraging advanced algorithms and large datasets.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. Utilizes domain knowledge and data exploration techniques to identify informative features.Deploy machine learning models into production environments, integrating them with existing systems and applications. Implements scalable, efficient, and reliable solutions for real‐time batch inference.Monitor model performance, reliability, and scalability in production environments, implementing 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 to include data scientists, software engineers, product managers, and other stakeholders to understand requirements, prioritize projects and deliver impactful AI solutions.Perform additional duties as assigned.May coach and provide guidance to less experienced professionals.May serve as a team or task lead.Works independently under general supervision.Required SkillsBachelor's degree in a relevant field and 5+ years of experienceAnalytical & ProgrammingStrong Python (data manipulation, model development; libraries like Pandas, NumPy, scikit‐learn)SQL proficiency (joins, window functions, performance‐aware queries)Statistical foundations (probability, hypothesis testing, regression, experimental design/A‐B testing)Data ModelingEnd‐to‐end ML workflow experience (feature engineering, training, validation, deployment, monitoring)Data wrangling & ETL/ELT (building reliable pipelines; handling messy, large datasets)Model evaluation (metrics selection, bias/variance trade‐offs, error analysis)AI Integration w/ MLOpsHands‐on API integration for AI services (e.g., calling model endpoints, building microservices)Production deployment of models (packaging, versioning, CI/CD for ML)Model monitoring (drift detection, performance tracking, retraining triggers)Cloud PlatformsExperience with at least one major cloud (Azure, AWS, or GCP) for data/AI workloadsFamiliarity with containers (Docker) and source control (Git)Data visualization skills (Power BI or Tableau) to communicate insights and outcomesCommunicationSystem analysis skills to identify viable AI insertion points in processes, products, or workflowsStakeholder communication (translating technical findings into business value and concrete recommendations)Documentation of models, assumptions, data lineage, and decisionsGovernance/SecurityResponsible AI awareness (fairness, explainability, privacy, and compliance considerations)Basic understanding of data security and access controls in production environmentsPreferred SkillsAdvanced AI/LLMExperience with LLMs (e.g., Azure OpenAI Service/OpenAI API) for summarization, classification, or copilotsPrompt engineering and evaluation of LLM outputs for quality and safetyRAG pipelines (retrieval‐augmented generation), vector databases (e.g., Azure AI Search, Pinecone, FAISS), and embeddingsFine‐tuning or model adaptation strategies for domain‐specific use casesMLOps EngineeringModel orchestration/experiment tracking (MLflow, Weights & Biases)Kubernetes and ML deployment tools (e.g., AKS/EKS, Argo, KServe)Feature stores, A/B testing frameworks, and event‐driven/streaming data (Kafka, Kinesis)CI/CD pipelines (GitHub Actions, Azure DevOps) and Infrastructure as Code (Terraform, Bicep)Data Platform IntegrationDatabricks, Snowflake, or BigQuery experienceBuilding robust APIs (REST/GraphQL) and microservices around modelsMonitoring & Observability (Prometheus, Grafana, app & model logs)Responsible AI & CompliancePractical experience with model risk management, documentation standards, and explainability (SHAP, LIME)Knowledge of privacy‐by‐design and PII handling (data minimization, anonymization)(If applicable to the environment) familiarity with FedRAMP or regulated environmentsAdditional Languages/ToolsR, PySpark, or Scala for data‐intensive workloadsLangChain or Semantic Kernel for LLM app developmentTableau/Power BI advanced (parameterized dashboards, Row‐Level Security)Ability to support 24x7 environment for business critical and contractual SLA impacting issuesClearanceCandidates must be eligible to obtain a federal security clearance.Company OverviewGDIT is 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. With over 30,000 experts, GDIT extracts 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.BenefitsOur benefits package for all U.S.-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. We encourage work/life balance, offering GDIT 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. 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 also provided or available. We regularly review our Total Rewards package to ensure it remains competitive and reflects what our employees value most.OpportunitiesExplore a career in data science and engineering at GDIT, and you'll find endless opportunities to grow alongside colleagues who share your determination for solving complex data challenges. The likely salary range for this position is $128,039 - $173,229. Salary will set based on experience, geographic location, and possibly contractual requirements, and could fall outside this range.Work DetailsScheduled Weekly Hours: 40Travel Required: NoneTelecommuting Options: HybridWork Location: USA DC WashingtonAdditional Work Locations:EEO StatementEqual Opportunity Employer / Individuals with Disabilities / Protected VeteransJoin Our Talent CommunityJoin our Talent Community to stay up to date on our career opportunities and events at gdit.com/tc.#J-18808-Ljbffr