Software Developer III (AI & ML)

As a Software Developer III at NISC, you will be responsible for designing, developing, and deploying AI-driven applications. Your role will involve training, fine-tuning, and optimizing machine learning models, as well as writing clean and scalable code. You'll also build efficient data processing pipelines and ensure that AI applications align with the needs of cooperatives and utilities.

To qualify for the Software Developer III role at NISC, applicants should have a minimum of 6 years in software development with at least 3 years focused on AI/ML. Experience with LLMs, Python, ML frameworks, and deploying AI solutions in cloud environments like AWS or Azure is highly desirable. A bachelor's degree in a relevant field is also recommended.

Candidates for the Software Developer III position at NISC should be proficient in Python and familiar with machine learning frameworks such as TensorFlow or PyTorch. An understanding of data processing, AI safety, agentic architectures, and successful deployment of AI projects in real-world applications will also be crucial.

NISC is committed to the personal and professional development of its employees. As a Software Developer III, you will have access to ongoing training opportunities, educational assistance programs, and a supportive work environment that encourages innovative thinking and collaboration with team members.

In the Software Developer III role at NISC, you'll work on projects that involve cutting-edge AI and machine learning applications. This includes implementing advanced models, optimizing AI agents, and designing intelligent architectures to better serve cooperatives and utilities in rural America.

When answering this question, detail a specific project where you overcame technical challenges, emphasizing your role in solution development and the impact it had on users or stakeholders. Highlight the technologies you used and any measurable results achieved.

Discuss your systematic approach to debugging by first checking data quality, ensuring proper feature processing, and evaluating model performance metrics. Describe techniques you use for model interpretation and insights you've gained from troubleshooting.

Share specific examples of models you've deployed, discussing the challenges you faced, how you ensured scalability, and the tools or frameworks you utilized for deployment and monitoring in production environments.

Explain your strategy for staying informed, which may include attending conferences, participating in online communities, reading the latest research papers, or completing relevant online courses. Share any specific resources or publications you find particularly useful.

Detail any projects that involved reinforcement learning, explaining the problems you aimed to solve and the outcomes. Discuss the algorithms you used and why they were suitable for the task, showing your understanding of this advanced area of AI.

Provide an example of a successful collaborative project, emphasizing communication strategies you employed and how you navigated challenges within the team. Highlight your ability to listen and integrate feedback to achieve a common goal.

Show your knowledge of agent-based systems by defining what agentic AI is, how it differs from traditional AI systems, and its applications. Mention any relevant projects you’ve completed that utilized this architecture and the benefits it provided.

Articulate your passion for AI and machine learning, especially how it can positively impact communities through improved service delivery. Share personal experiences or values that align with NISC's mission to inspire your commitment to the role.

Discuss various metrics and methods you would use to evaluate performance, such as precision, recall, F1 score, and AUC. Describe processes for iterative testing and validation to ensure reliability and usability over time.

Emphasize the importance of data privacy in AI development. Discuss compliance with regulations, ethical considerations, and strategies you employ to ensure user data is handled responsibly throughout the design and deployment phases.