AI/ML Engineer
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Role details
Tech stack
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Job description
The AI/ML Engineer collaborates closely with data scientists, dashboard teams, developers, and PMO leadership to ensure AI/ML models are usable, optimized, and appropriately embedded into mission workflows., The AI/ML Engineer will: Develop rapid prototypes, microservices, APIs, widgets, and integrations to validate new AI/ML concepts.
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Integrate ML models into dashboards, applications, and operational workflows.
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Build lightweight UI components that demonstrate how AI outputs can be consumed.
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Optimize ML pipelines for performance, scalability, and efficient inference.
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Collaborate with AI/ML Architects and Data Scientists to operationalize models and automation logic.
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Support data ingestion, feature engineering, and preprocessing tasks for prototype development.
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Build API wrappers, routing logic, and connector services for ML model interactions.
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Validate prototype functionality, performance, error handling, and model integration.
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Contribute to documentation of models, prototypes, integrations, and deployment patterns.
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Work with cybersecurity teams to ensure integration patterns align with security requirements.
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Recommend improvements based on usability testing, technical feedback, and mission needs.
Requirements
Candidate must possess a Tier 2 Moderate Risk Public Trust (from any federal agency) or an active Secret clearance or higher.
Education (Required)
Bachelor’s degree in Data Science, Computer Science, Artificial Intelligence/Machine Learning, Statistics, or a related field.
Experience (Required)
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Minimum 5 years of experience in software development, AI/ML engineering, or data-driven application development.
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Experience building API-driven prototypes, integrations, or microservices.
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Experience integrating ML models into dashboards, applications, or mission workflows.
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Experience working with data scientists or ML engineers to transition models to production.
Technical Knowledge (Required)
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Proficiency with Python, JavaScript, or similar development languages.
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Familiarity with ML frameworks (TensorFlow, PyTorch, Scikit-Learn).
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Experience building REST APIs, web services, or integration layers.
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Understanding of data pipelines, preprocessing steps, and ML lifecycle workflows.
Technical Knowledge (Preferred)
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Experience with AWS cloud services or ML platforms.
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Familiarity with front-end frameworks for rapid prototyping.
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Experience with CI/CD pipelines and DevSecOps tooling.
Certifications
Required:
- ITIL v4 Foundation
Preferred:
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AI/ML engineering or cloud certifications
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Scrum Master or Agile certifications
Skills
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Strong development and prototyping skills.
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Ability to translate AI/ML concepts into functional components quickly.
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Strong collaboration skills with cross-functional technical teams.
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Excellent communication and documentation skills.
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High attention to detail in integration, testing, and optimization work.
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