Product Manager - Edge (TSSCI)

The Product Manager - Edge (TSSCI) at VivSoft is responsible for monitoring the funding and development of technical solutions required for integrating AI/ML on DoD platforms. Additionally, they manage the installation events and developmental tests while interfacing with acquisition stakeholders to align tech roadmaps and deconflict schedules. This role is also crucial in generating proposals for advanced AI R&D efforts and coordinating exercise demonstrations of AI/ML capabilities.

Candidates for the Product Manager - Edge (TSSCI) position at VivSoft need to have an active TS/SCI clearance, a bachelor's degree in engineering, Computer Science, or a related discipline. A broad knowledge of AI model development processes and the ability to communicate effectively with various stakeholders is crucial. Additional desirable qualifications include experience in managing diverse teams and a strong understanding of software quality, risk management, and compliance.

Essential skills for the Product Manager - Edge (TSSCI) role at VivSoft include exceptional communication abilities, both verbal and written, and a strong capacity for relationship-building across disciplines. Furthermore, candidates should demonstrate diverse technical experience, with a solid understanding of AI model development and data modeling tools, all while being capable of managing competing priorities in a fast-paced, compliance-driven environment.

While not mandatory, experience working in a joint military environment can be a significant asset for the Product Manager - Edge (TSSCI) role at VivSoft. This experience helps candidates better understand the unique operational and tactical challenges that military stakeholders face, enhancing their ability to interface and align technology solutions with warfighter needs.

At VivSoft, candidates can expect a collaborative and mission-focused culture that values diversity and innovative thinking. The team comprises strategists, engineers, and creators passionate about building scalable solutions. Employees are encouraged to embrace automation, work with cutting-edge technologies, and continually strive for excellence, all while enjoying a supportive and inclusive work environment.

When answering this question, share specific projects where you were involved in AI model development. Discuss the methodologies you used, challenges you faced, and how you overcame them. Highlight your understanding of AI cycles and your ability to communicate complex ideas to varied audiences.

In your response, emphasize your approach to communication and collaboration. Talk about strategies you use, such as stakeholder mapping and regular check-ins, to ensure that all voices are heard and aligned towards a common goal. Illustrate with an example if possible.

Discuss your leadership style and how you foster an environment of trust and innovation. Include specific examples of how you've motivated teams and resolved conflicts to keep projects on track. Mention any tools you use for project management and team collaboration.

Prepare to share a relevant experience where you successfully adapted to project changes. Focus on your problem-solving skills, your ability to remain flexible, and how you communicated this change to your team. Highlight the positive outcomes that resulted.

Share your understanding of compliance frameworks relevant to AI projects. Discuss your prior experiences ensuring compliance and the specific measures you take to mitigate risks. If applicable, mention specific tools or methodologies you are familiar with.

Explain your process for gathering and analyzing stakeholder feedback. Discuss how you prioritize this feedback and incorporate it into project plans. Use examples from your past experience to demonstrate your approach's effectiveness.

Mention specific resources you rely on, such as industry publications, forums, or conferences. Describe how you integrate new knowledge into your work practices and share it with your team, emphasizing the importance of continuous improvement.

Demonstrate your understanding of the technical, operational, and ethical challenges involved in AI/ML integration. Discuss how you would approach these challenges creatively, using both your technical knowledge and your interpersonal skills to collaborate with teams.

Discuss the key performance indicators (KPIs) you would use to measure success in AI initiatives. Mention both quantitative metrics, such as performance improvements, and qualitative metrics, such as user satisfaction. Highlight the importance of aligning these metrics with organizational goals.

Explain how you identify and assess risks throughout the project lifecycle. Discuss techniques you use for risk mitigation and give examples of how effective risk management has contributed to successful project outcomes in your experience.