Software Developer III (AI & ML)

As a Software Developer III (AI & ML) at NISC, you will be primarily responsible for designing, developing, and deploying AI-powered applications. Your work will include integrating advanced machine learning models, training and fine-tuning these models for production environments, and ensuring that the solutions align with the needs of cooperatives and utilities. You'll also develop efficient data processing pipelines and contribute to AI solutions that have a real-world impact.

To qualify for the Software Developer III (AI & ML) position at NISC, candidates should have at least 6 years of software development experience, including 3 years specifically in AI/ML software development. Key technical skills include strong proficiency in Python, experience with ML frameworks like TensorFlow or PyTorch, and familiarity with deploying AI solutions in production environments. A Bachelor’s degree in Computer Science or a related field is preferred.

NISC is committed to personal and professional development for its employees. As a Software Developer III (AI & ML), you will have access to ongoing training programs, including a $25,000 educational assistance program. We encourage a culture of continuous learning and provide resources to help you stay abreast of advancements in AI and software development best practices.

In the role of Software Developer III (AI & ML) at NISC, you'll work on innovative AI projects that directly support utilities and broadband providers. Your projects may involve developing AI-driven applications that enhance customer service and operational efficiency in cooperatives. You'll also have the opportunity to implement LLMs and explore multi-agent systems as part of our commitment to leveraging AI technology for community empowerment.

The work environment at NISC for a Software Developer III (AI & ML) is flexible and collaborative. While we promote a hybrid work model, requiring at least three days a week in the office, there are also opportunities for fully remote work for qualified candidates. Our culture emphasizes teamwork, innovation, and employee well-being, creating a supportive atmosphere for all team members.

When answering this question, focus on specific projects where you took an AI model from concept to deployment. Discuss the tools and frameworks you used, the challenges you faced, and how you ensured scalability and maintainability. Provide examples of how these models improved user experience or business outcomes, demonstrating your practical expertise.

To address this question, provide concrete examples of how you have worked with LLMs, including fine-tuning processes and prompt engineering. Discuss the specific outcomes of your integration efforts, and how these models added value to the applications or systems you developed.

In your answer, highlight the importance of continuous monitoring and evaluation of ML models after deployment. Discuss techniques such as performance tuning, updating training datasets, or implementing reinforcement learning strategies to improve model accuracy and user satisfaction.

Use the STAR method (Situation, Task, Action, Result) to organize your response. Detail the context of the challenge, your role in the project, the specific actions you took to address the issue, and the positive outcome that resulted from your efforts.

Highlight the importance of data preprocessing, including data cleaning and normalization techniques. Discuss how you handle missing data, outlier detection, and the methods you implement to ensure your training dataset is representative of real-world scenarios to foster model reliability.

In your response, discuss the resources you consult regularly, such as academic papers, online courses, and AI conferences. Highlight your commitment to continuous learning and how you apply new knowledge to enhance your projects and contribute effectively to your team.

Focus on specific instances where you collaborated on projects, emphasizing teamwork and communication. Explain how you tailor your communication style for different audiences, from technical team members to non-technical stakeholders, ensuring all parties understand complexities and objectives.

Provide a concise definition of agents in AI, discussing their behavior and decision-making capabilities. When discussing multi-agent systems, emphasize your problem-solving approach, collaboration between agents, and how you design systems that leverage these interactions effectively to achieve desired outcomes.

Outline a specific scenario where a machine learning model underperformed, detailing the steps you took to diagnose the issue. Discuss the analysis you conducted, the adjustments you made, and how your troubleshooting led to a significant improvement in model performance.

Discuss your process for understanding the organization's goals, values, and target community needs. Explain how you incorporate stakeholder feedback and align your AI development with these objectives to create meaningful impact through your solutions.