Government Sales Executive job

The questions outlined in this module are designed to guide teams that are building and implementing AI systems but are not official standards or policy. Rather, they are good questions to consider as your team progresses through the AI lifecycle to begin to embed responsible and trustworthy AI in the process. These questions are intended to foster discussions around broad ethics topics. Ask these questions often through the design-develop-deploy cycle and combined with testing to help reduce unintended consequences. Identifying AI use cases and the data required for them can be specific and localized. Further, the nature of algorithms and model training can require varying degrees of customization as the data is aggregated, cleansed, assimilated, and the outcomes are generated.

Monitor the outcomes

Agencies with limited in-house computing resources need a cloud platform; so do ML models that require intense computing resources for training, such as for Deep Learning and GPU acceleration. A cloud platform can be more economical when the computing requirements are short-term and sporadic, depending on data security requirements. Certainly, organizing an agency for successful AI development can be a challenge. With the organizational structure we’ve already shared in mind, let’s shift our focus to the actual personnel and staffing required to implement an AI project. The central AI technical resource that provides tooling and security, legal, and acquisition support, combined with mission-based AI practitioners, make up an AI-enabled organization. Infrastructure as code (IaC) brings the repeatability, transparency, and testing of modern software development to the management of infrastructure such as networks, load balancers, virtual machines, Kubernetes clusters, and monitoring.

Focus on agency mission

• It’s useful when subject matter experts are unsure of common properties of a data set.
• Program evaluation and support functions are also included in AIOps to show the operational impact of AI investments.
• This is meant to be an evolving guide to the application of AI for the U.S. federal government, because AI is a rapidly evolving set of technologies.
• But this opportunity requires investing in formal education for these early-career practitioners in order to realize their full potential.

The requirements must feature the ways in which the results from the model will be evaluated, used, and updated. Prototyping internally can help identify where in the life cycle to seek a vendor. It could reveal either that you should engage early to turn an internal prototype into a pilot, or that you should develop a pilot before engaging a vendor for full-scale production. To find the right AI solution or vendor, leaders must know the field from meeting with companies in person, calling people, and doing market research. This vital systematic approach is improved with professional company assessors. Ultimately, the decision to buy or build AI is informed by what is available in the market and the team’s internal capabilities.

Artificial Intelligence (AI) and Machine Learning (ML)

For example, the central AI resource knows whether certain programming languages or certain hardware capabilities are prevalent. If there’s a strategic decision to increase certain platforms or skill sets, the AI resource knows how to do that. While the agency’s HR office is still ultimately in charge of all workforce recruitment, the AI resource works closely with HR to provide the AI domain expertise. Government Sales Executive (AI project) job AI practitioners, even if they love the agency’s mission, expect to actually practice AI in their jobs. That’s why the supportive and powerful work environment that the central AI resource provides is just as important to the pitch as creating space for AI practitioners in mission areas and program offices. They should not assume that only people with computer science or statistics education are going to be appropriate for AI-centric positions.

Organizational Maturity for MLOps

SMART scholars receive full tuition, annual stipends, and guaranteed employment with the DOD after graduation. Build and invest in infrastructure and oversee federal R&D to enable the next generation of cutting-edge AI systems in the U.S. To find about how we process your personal data and your rights regarding the processing of your personal data, please see our Privacy Policy. At this stage, the system is usually tested under realistic operational conditions and with the operator. This is where we learn about the system’s mission effectiveness, suitability, and survivability.

Start with people

• One example of this is a central team of data scientists that can be loaned to a specific mission center or program office.
• Unsupervised learning is often used in data exploration before a learning goal is established.
• Answering the “buy or build” question depends on the program office’s function and the nature of the commercial offering.
• Continuous performance improvement of activities through incremental and innovative improvements.
• While minimum tagging requirements vary across different organizations, the list above is provided as a general guideline.
• This guide gives leaders enough information to make the right AI investments.

AI practitioners who feel they may be falling behind in their field while working in government are more likely to leave to maintain their own competence as AI practitioners; agencies should actively prevent this situation to improve retention. But while these factors are still true, there is no practical increase in any of those resources that would itself suffice to address the new information volumes. With thousands or millions of pages of documents, we could never even try to hire enough staff to read through them all.

An important part of assessing an organization’s existing talent is acknowledging that some people may already be leveraging defined AI and ML skills. Others, however, may work in technical roles or have skills that are not directly AI https://wizardsdev.com/en/vacancy/junior-qa-at-enginner-c/ related, but could easily be supplemented to become AI skills. This chapter will discuss what an Integrated Product Team might look like, how to build and manage AI talent, and how to develop learning programs that cultivate transformational AI capabilities.

Agencies’ ultimate goal should be to create self-service models and shareable capabilities rather than pursuing contractors’ made-to-order solutions. Agencies must weigh the benefits and limitations of building or acquiring the skills and tools needed for an AI implementation. Answering the “buy or build” question depends on the program office’s function and the nature of the commercial offering. If there are existing data, analytics, or even AI teams, align them to the identified use cases and the objective of demonstrating mission or business value. If there are no existing teams, your agency may still have personnel with relevant skill sets who haven’t yet been identified. Survey the workforce to find this talent in-house to begin building AI institutional capability.