Sr. AI/ML Engineer I (6475)

As Sr. AI/ML Engineer I, you’ll design, develop, and deploy machine learning solutions supporting secure, web-based applications in a dynamic, mission-focused environment. This role emphasizes applied AI, experimentation, and collaboration with cross-functional teams to deliver production-ready capabilities that evolve as mission needs change.

Responsibilities

• Design, develop, implement, and fine-tune AI and machine learning models to support web-based applications in secure environments with evolving use cases.
• Build and maintain data pipelines, training workflows, and experimentation environments to enable rapid model iteration and evaluation.
• Evaluate model performance using quantitative and qualitative metrics (e.g., accuracy, robustness, stability, efficiency, generalization) and translate results into actionable improvements.
• Analyze data, model outputs, and experimental results to recommend changes to algorithms, features, data sources, or system architecture.
• Proactively identify and assess tools, frameworks, and technologies that best support platform goals, balancing performance, scalability, and maintainability.
• Collaborate closely with software developers, data engineers, DevSecOps teams, and stakeholders to integrate AI capabilities into production systems.
• Ensure AI and data science solutions are transparent, testable, and maintainable to support long-term operational use.
• Communicate technical approaches, assumptions, tradeoffs, and results clearly to both technical and non-technical audiences, including during design reviews and demonstrations.

Requirements

• An Active Secret security clearance
• Bachelor's degree in Computer Science, Engineering, Data Science or related technical discipline.
• 4+ years of experience building and managing ETL and ELT data pipelines within Databricks environment
• Hands-on experience with Python and SQL and libraries such as TensorFlow, PyTorch, Scikit-learn
• Experience deploying models on cloud platforms (such as AWS, GCP, Azure) and containerization tools (Docker, Kubernetes)
• Experience managing model deployment and monitoring (MLOps, MLflow, Kubeflow, etc.)
• Knowledge of data modeling, neural network architectures, and software development and CI/CD best practices
• Must be willing/able to travel to customer, as needed

Pay

The salary range for this position is determined based on qualifications, skills, and relevant experience. The final salary offered will be determined based on several factors including:

• The candidate's professional background and relevant work experience
• The specific responsibilities of the role and organizational needs
• Internal equity and alignment with current team compensation

$138,000 – $184,000