AI Architect / 1

As an AI Architect at Inetum Polska, your main duties include designing a scalable Customer Care Platform powered by AI that utilizes Azure AI services and Genesys Cloud on AWS. You'll be involved in integrating Natural Language Processing and Machine Learning technologies to automate customer interactions effectively. Additionally, your responsibilities will encompass defining and implementing a microservices architecture, collaborating with DevOps teams, and optimizing AI models to enhance service quality.

To qualify for the AI Architect position at Inetum Polska, candidates should have a degree in Computer Science or a related field along with substantial experience in architecting AI-driven cloud applications, preferably using the Azure AI stack and AWS. Strong skills in Conversational AI, Natural Language Processing, and Machine Learning are essential, along with a solid understanding of microservices architecture and DevOps processes.

Inetum Polska is committed to fostering professional growth by fully funding training, certifications, and attendance at technology conferences. As an AI Architect, you'll receive ongoing guidance from a dedicated Team Manager and tailored mentoring from a technical leader based on your expertise and project requirements, ensuring you stay at the cutting edge of technology.

Inetum Polska prides itself on a supportive and inclusive work culture that emphasizes diversity, mutual respect, and community involvement. Employees are encouraged to participate in team-building events and social initiatives, creating a lively atmosphere where AI Architects can collaborate and thrive while feeling valued and respected.

Inetum Polska offers a flexible and hybrid work model for AI Architects, allowing employees to split their time between home and modern offices in key Polish cities like Warsaw, Poznan, and Lublin. With flexible working hours and a focus on balancing personal and professional life, Inetum ensures you can perform at your best while enjoying a comfortable working environment.

In your answer, highlight specific projects you've worked on, the AI technologies you utilized, and how you approached challenges in the design process. Be sure to emphasize how you ensured scalability and compliance while focusing on user experience.

Discuss any specific projects where you utilized NLP, the tools and frameworks you employed, and the results achieved. Provide examples that illustrate your understanding of NLP and its impact on customer interactions.

Provide details about the Azure AI services you've worked with, such as Azure OpenAI and Azure Cognitive Services. Explain how you integrated these services into your applications, the outcomes, and any challenges you overcame during implementation.

Your response should cover best practices such as monitoring system performance, implementing robust security protocols, and using tools for API management. Mention how you address these concerns during the design phase of a project.

Discuss the methodologies you apply for monitoring AI models in production, such as performance metrics and user feedback. Highlight how you incorporate continuous learning to improve model accuracy and effectiveness.

Select a specific challenge related to AI architecture you encountered, outline the steps you took to analyze and address it, and describe the final outcome. Emphasize your problem-solving skills and teamwork.

Discuss your experience working with DevOps teams and the practices you find effective for ensuring smooth collaboration. Highlight the importance of using CI/CD pipelines and communicating technical requirements clearly.

Mention resources such as online courses, webinars, technical blogs, and newsletters that keep you informed about AI trends. Tie your response to your commitment to lifelong learning and applying new knowledge to your work.

Explain how you incorporate API strategies into your architectural plans to ensure seamless integration with existing systems. Discuss your approach to API management and the importance of effective documentation.

Provide a descriptive overview of a project involving Machine Learning, including your role, challenges faced, and how you successfully integrated ML into the product. Highlight the outcome and learning experiences from the project.