AI/ML Engineer Lead

CACI International•Aberdeen Proving Ground, MD

2d•$126,100 - $277,300•Onsite

About The Position

Lead artificial intelligence and machine learning engineering efforts for Department of Defense intelligence, electronic warfare, and cyber systems. As an AI/ML Engineer Lead, you will develop and implement advanced AI/ML algorithms, predictive analytics, and decision support capabilities for intelligence and mission systems. You will lead data science initiatives including data mining, statistical modeling, and machine learning to extract insights from large structured and unstructured datasets. You will architect and implement AI/ML pipelines, MLOps workflows, and automated analytics solutions for intelligence exploitation. You will develop Multi-INT data fusion capabilities, geospatial analytics, and intelligence processing automation. You will lead the integration of AI/ML capabilities into mission applications and operational systems. You will provide technical leadership and mentorship to data science and AI/ML engineering teams. You will interface with systems engineers, software developers, and government stakeholders on AI/ML architecture and implementation.

Requirements

Active Secret security clearance (required)
Master's degree in Computer Science, Data Science, Mathematics, Statistics, Engineering, or related quantitative field
10+ years of professional experience with data science and AI/ML engineering
Strong programming skills in Python, R, or similar languages commonly used in data science applications
Demonstrated experience developing and deploying machine learning models in production environments
Experience with ML frameworks and libraries such as TensorFlow, PyTorch, Scikit-learn, Keras, or similar platforms
Strong understanding of statistical methods, probability theory, and mathematical modeling
Experience with data manipulation and analysis using tools such as Pandas, NumPy, and SciPy
Knowledge of SQL and NoSQL databases and experience with data querying and database design
Experience with data visualization tools and techniques using tools such as Matplotlib, Seaborn, Tableau, or similar platforms
Strong analytical and problem-solving skills with ability to extract insights from complex datasets
Excellent communication skills with ability to explain complex technical concepts to non-technical stakeholders
Demonstrated ability to lead technical teams and mentor junior data scientists

Nice To Haves

Active Top Secret clearance with SCI eligibility
Ph.D. in Data Science, Computer Science, Mathematics, Statistics, or related field
Relevant industry certifications such as AWS Certified Machine Learning Specialty, Google Professional Machine Learning Engineer, or Microsoft Certified: Azure AI Engineer
Experience with intelligence community systems and intelligence data formats
Knowledge of SIGINT, GEOINT, or Multi-INT data processing and analytics
Experience with distributed computing frameworks such as Apache Spark, Hadoop, or Dask
Familiarity with deep learning architectures including CNNs, RNNs, LSTMs, and Transformer models
Experience with natural language processing (NLP) and text analytics for intelligence documents
Knowledge of computer vision and image processing for geospatial or imagery intelligence
Experience with MLOps tools such as MLflow, Kubeflow, or similar platforms
Understanding of cloud platforms such as AWS, Azure, or on-premises cloud solutions for AI/ML workloads
Experience with containerization technologies (Docker, Kubernetes) for model deployment
Knowledge of graph analytics and network analysis for intelligence applications
Experience with time series analysis and forecasting methods
Understanding of reinforcement learning and optimization algorithms
Familiarity with explainable AI (XAI) and model interpretability techniques
Experience with DevSecOps practices and CI/CD pipelines for AI/ML deployments
Knowledge of adversarial machine learning and AI security considerations
Previous experience supporting government program offices in defense or intelligence sectors

Responsibilities

Design and develop machine learning models using supervised and unsupervised learning techniques for intelligence analysis and automation
Implement natural language processing (NLP), computer vision, and deep learning solutions for intelligence data exploitation
Develop and maintain data pipelines using ETL processes, API integration, and distributed computing frameworks
Build predictive analytics models and decision support tools to enhance intelligence operations and mission effectiveness
Implement MLOps best practices including model versioning, automated testing, continuous integration, and deployment automation
Apply statistical methods, graph algorithms, and advanced analytics to support intelligence objectives
Develop data visualization dashboards and interactive analytics tools for intelligence analysts and operators
Work with large-scale data architectures including SQL and NoSQL databases, data lakes, and cloud-based analytics platforms
Integrate AI/ML capabilities with intelligence data platforms and enterprise data fabrics
Conduct exploratory data analysis, feature engineering, and model validation to ensure accuracy and performance
Develop technical documentation including model specifications, data science methodologies, and performance metrics
Collaborate with cybersecurity teams to ensure secure AI/ML implementations and protect against adversarial attacks
Research emerging AI/ML technologies and recommend innovative solutions for intelligence modernization
Present findings and recommendations to technical teams and senior leadership