AI Engineer

As an AI Engineer at Netguru, your responsibilities will include implementing the Azure OpenAI API, developing and deploying machine learning models, and collaborating with cross-disciplinary teams. You'll also lead AI project implementations, fine-tune open-source models for clients, and innovate with various machine learning techniques. This role offers a fantastic opportunity to make a significant impact within our projects across industries, such as healthcare and finance.

To qualify for the AI Engineer position at Netguru, candidates should have 2-5 years of commercial software development experience, proficiency in Python, and familiarity with AI frameworks like PyTorch and TensorFlow. A strong understanding of the Azure OpenAI API, RAG systems, and database optimization techniques is crucial. Additionally, candidates should have excellent communication skills in English and be based in the European Union.

AI Engineers at Netguru work with a variety of tools and technologies, including Azure OpenAI API, PyTorch, TensorFlow, and frameworks like autogen and guidance. Familiarity with big data technologies such as Spark and Hadoop is a plus. You'll also engage in cloud-based AI development using platforms like AWS and Azure, ensuring you stay at the forefront of industry advancements.

As an AI Engineer at Netguru, you'll have the chance to work on a diverse range of projects, from creating proof of concepts (PoCs) to developing minimum viable products (MVPs) for AI applications. The role involves applying AI in various sectors including retail, healthcare, and finance, thereby giving you a rich variety of experiences to enhance your skill set.

Netguru offers a competitive salary for AI Engineers, along with a range of benefits including access to the WorkSmile platform for personalized perks, discounts on Apple products, and a one-time home office bonus for B2B contractors. Additionally, remote employees receive a monthly lump sum for expenses, and there are regular internal initiatives like webinars and knowledge-sharing sessions to promote professional growth.

When answering this question, focus on specific projects where you implemented the Azure OpenAI API, detailing the challenges faced and how you overcame them. Highlight any successful outcomes that resulted from your work, showcasing your ability to apply the API in real-world situations.

Discuss the techniques you are most familiar with, such as supervised vs. unsupervised learning or reinforcement learning, and provide examples of projects or tasks where you effectively applied these techniques. This will not only show your knowledge but also your practical experience.

Explain your general approach to designing machine learning models, starting from problem definition to data preprocessing and feature engineering, model selection, and evaluation. Illustrate your answer with a relevant example to demonstrate your understanding.

Share examples of your work with RAG systems, explaining your understanding of how they operate and the specific contributions you made to projects involving these systems. Mention any tools or frameworks you used and the outcomes.

Discuss your approach to mentoring, including how you support junior engineers in their development. Provide examples of instances where you've successfully guided a junior teammate, emphasizing your leadership and communication skills.

Share your strategies for staying informed about the rapidly-evolving landscape of AI technology, whether it's through online courses, attending conferences, subscribing to relevant journals, or contributing to open source projects. This reflects your commitment to continuous learning.

Share your perspective on the value of open-source models. Highlight your experience working with them, focusing on the benefits they provide in terms of innovation, cost-effectiveness, and community support, while also addressing potential challenges.

Choose a specific project that presented significant challenges, explaining what those challenges were and the steps you took to address them. Focus on the skills you utilized and what you learned from the experience.

Discuss the critical role feature engineering plays in enhancing model accuracy and performance. Provide examples of techniques you have employed in past projects and how they positively impacted the outcomes.

Describe the metrics and methods you use to evaluate machine learning models, such as confusion matrices, ROC curves, or precision-recall metrics. Highlight your experience with these evaluation techniques to show your analytical skills.