Artificial Intelligence Developer/Engineer

As an Artificial Intelligence Developer/Engineer at NEORIS, your main responsibilities will include implementing end-to-end generative AI solutions, understanding the limitations of AI capabilities while setting realistic expectations, and navigating the complete lifecycle of model development. You'll also focus on ethical alignments to ensure fair outcomes, define measurable KPIs for model accuracy and business impact, and work on user experience to guarantee the clarity and relevance of generated responses.

Candidates interested in the Artificial Intelligence Developer/Engineer role at NEORIS should possess strong expertise in various LLMs (like GPT and Geminis), knowledge of prompt engineering, and skills in text retrieval optimization. Additional experience with CI/CD pipeline development and implementing guardrails for validation will be crucial for ensuring high-quality results in your work.

At NEORIS, the work environment for an Artificial Intelligence Developer/Engineer is flexible and collaborative, with opportunities for remote work. We prioritize a balance between professional growth and personal well-being, encouraging team collaboration and creativity while upholding our values of responsibility and innovation within our Generative AI Hub.

Artificial Intelligence Developer/Engineers at NEORIS enjoy a permanent contract with a comprehensive benefits package that includes social benefits, flexible remuneration options, and remote working opportunities. We also promote flexible hours to foster work-life balance, making it easier for our employees to navigate their professional and personal commitments.

To prepare for an interview for the Artificial Intelligence Developer/Engineer position at NEORIS, focus on brushing up on your understanding of LLMs, the AI development lifecycle, and ethical considerations in AI. Be ready to discuss examples of your past experiences related to prompt engineering and CI/CD pipelines, showing how you've implemented these strategies effectively in previous projects.

When discussing your experience with LLMs during the interview, provide specific examples of projects where you utilized models such as GPT or Geminis. Explain the context of the project, your role, and how you optimized performance or addressed challenges related to model deployment.

In response to this question, discuss the frameworks or guidelines you follow to ensure ethical AI use. Mention the importance of fair training data, transparency in model decisions, and steps you take to avoid biased outcomes, illustrating your commitment to responsible AI.

Explain your familiarity with CI/CD principles, emphasizing the importance of automation in testing and deploying AI models. Discuss specific tools you've used and outline the steps you take to ensure smooth integration and continuous delivery of AI functionalities in production environments.

Talk about the key performance indicators (KPIs) you consider critical, such as response accuracy, user engagement, and time efficiency. Provide examples of how you've measured these factors in your work and any adjustments you made based on the findings.

Sharing your experiences with challenges is key. Provide a specific example of a technical or ethical challenge you faced, detailing how you approached the issue, the solutions you implemented, and what the outcomes were, highlighting your problem-solving skills.

Describe your approach to tool feedback loops in AI projects, emphasizing how you integrate user feedback for continuous improvement. Discuss specific instances where user input led to changes in the model or its outputs and the processes you put in place to incorporate feedback efficiently.

Identify pitfalls you’ve encountered in generative AI projects, such as overfitting or biased training data. Discuss strategies you apply to mitigate these risks, showing your proactive mindset in ensuring high-quality and equitable AI outputs.

Highlight a successful project where you played a leading role. Explain the objectives, your contributions, and the results achieved, focusing on the positive business or user impacts resulting from the project to show your effectiveness as a developer.

Demonstrate your commitment to professional development by discussing your methods for staying informed, such as participating in online courses, attending conferences, following industry publications, and engaging in forums or communities focused on AI advancements.

Emphasize the importance of collaboration in AI development, explaining how teamwork fosters innovation, enhances problem-solving, and leads to better model performances. Provide examples of successful team projects and your approach to effective communication and coordination.