Description

• Collaborate with data scientists and other AI professionals to augment digital transformation efforts by identifying and piloting use cases. Discuss the feasibility of use cases along with architectural design with business teams and translate the vision of business leaders into realistic technical implementation. At the same time, bring attention to misaligned initiatives and impractical use cases.
• Align technical implementation with existing and future requirements by gathering inputs from multiple stakeholders — business users, data scientists, security professionals, data engineers and analysts, and those in IT operations — and developing processes and products based on the inputs.
• Play a key role in defining the AI architecture and selecting appropriate technologies from a pool of open-source and commercial offerings. Select cloud, on-premises or hybrid deployment models, and ensure new tools are well-integrated with existing data management and analytics tools.
• Audit AI tools and practices across data, models and software engineering with a focus on continuous improvement. Ensure a feedback mechanism to assess AI services, support model recalibration and retrain models. 
• Work closely with security and risk leaders to foresee and overturn risks, such as training data poisoning, AI model theft and adversarial samples, ensuring ethical AI implementation and restoring trust in AI systems. Remain acquainted with upcoming regulations and map them to best practices.

• Conduct data integration.
• Investigate flexible analytics solutions visualization, model building, operational decision-making, and decision capture capabilities.
• Supports the development of secure, rapid building, training, evaluation, debugging, and deployment of AI models across real world and theoretical scenarios.
• Identify portable solutions that can support distributed, vehicle mounted and on-premises use cases.
• Apply industry standards, data strategies and governance, data cataloguing, and DataOps practices for tactical systems.
• Enhance current AI/ML strategies.
• Assess and provide recommendations for survivable Edge Computing solutions that support deployment/employment of AI and ML to the tactical edge.
• Build security practices and tools into edge deployments.
• Validate AI commercial vendor products.
• Develop deep learning models.
• Translate operational needs into ML requirements.
• Evaluate and assess ML model performance.
• Identify and assess bias in externally developed models.
• Conduct statistical data analysis.
• Develop the user interface for deploying ML models.
• Leverage a cloud infrastructure for developing and deploying ML models.

• Bachelor’s degree in Computer Science, Statistics, Mathematics, Computer Engineering, or related field.
• Knowledge and experience in statistical and data mining techniques.
• Experience with distributed data and computing tools.
• Experience creating and using advanced machine learning algorithms and statistics: regression, simulation, scenario analysis, modeling, clustering, decision trees, neural networks, etc.
• Experience visualizing and presenting data for stakeholders.
• Cloud Computing certifications (e.g. Azure Administrator, AWS Associate Level Cert, etc.).
• Experience with on-premises cloud technologies (e.g. Azure Stack, Amazon Snow, etc.)