AI/ML Engineer

General Dynamics Information Technology • Washington, DC

36d • Hybrid

About The Position

The 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 delivery 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 To qualify, you must meet these basic qualifications: Required Skills Bachelor’s degree in relevant field and 5+ years of experience Analytical & Programming Strong 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 Modeling End-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/ MLOps Hands-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 Platforms Experience with at least one major cloud (Azure, AWS, or GCP) for data/AI workloads. Familiarity with containers (Docker) and source control (Git). Data visualization skills (Power BI or Tableau) to communicate insights and outcomes. Communication System analysis skills to identify viable AI insertion points in processes, products, or workflows. Stakeholder communication (translating technical findings into business value and concrete recommendations). Documentation of models, assumptions, data lineage, and decisions. Governance/Security Responsible AI awareness (fairness, explainability, privacy, and compliance considerations). Basic understanding of data security and access controls in production environments.

Requirements

• Bachelor’s degree in relevant field and 5+ years of experience
• Analytical & Programming Strong 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 Modeling End-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/ MLOps Hands-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 Platforms Experience with at least one major cloud (Azure, AWS, or GCP) for data/AI workloads.
• Familiarity with containers (Docker) and source control (Git).
• Data visualization skills (Power BI or Tableau) to communicate insights and outcomes.
• Communication System analysis skills to identify viable AI insertion points in processes, products, or workflows.
• Stakeholder communication (translating technical findings into business value and concrete recommendations).
• Documentation of models, assumptions, data lineage, and decisions.
• Governance/Security Responsible AI awareness (fairness, explainability, privacy, and compliance considerations).
• Basic understanding of data security and access controls in production environments.

Nice To Haves

• Advanced AI/LLM Experience with LLMs (e.g., Azure OpenAI Service/OpenAI API) for summarization, classification, or copilots.
• Prompt engineering and evaluation of LLM outputs for quality and safety.
• RAG pipelines (retrieval-augmented generation), vector databases (e.g., Azure AI Search, Pinecone, FAISS), and embeddings.
• Fine-tuning or model adaptation strategies for domain-specific use cases.
• MLOps Engineering Model 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 Integration Databricks, Snowflake, or BigQuery experience.
• Building robust APIs (REST/GraphQL) and microservices around models.
• Monitoring & Observability (Prometheus, Grafana; app & model logs).
• Responsible AI & Compliance Practical 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 environments.
• Additional Languages/Tools R, PySpark, or Scala for data-intensive workloads.
• LangChain or Semantic Kernel for LLM app development.
• Tableau/Power BI advanced (parameterized dashboards, Row-Level Security).
• Ability to support 24x7 environment for business critical and contractual SLA impacting issues

Responsibilities

• 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 delivery 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

Benefits

• Growth: AI-powered career tool that identifies career steps and learning opportunities
• Support: An internal mobility team focused on helping you achieve your career goals
• Rewards: Comprehensive benefits and wellness packages, 401K with company match, and competitive pay and paid time off
• Flexibility: Full-flex work week to own your priorities at work and at home
• Community: Award-winning culture of innovation and a military-friendly workplace
• 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.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level