AI Engineer

As an AI Engineer at Broadvoice, your main responsibilities will include researching and experimenting with large language models (LLMs) to identify applications that enhance automation and customer interactions. You will develop and fine-tune LLM-powered solutions, work on chatbots and intelligent assistants, and collaborate with product teams for seamless integration into our offerings. Staying updated on emerging AI technologies will also be crucial as you assess third-party tools to aid in your efforts.

To qualify for the AI Engineer position at Broadvoice, candidates should possess a Master's degree in Artificial Intelligence, Computer Science, Data Science, or a related field or equivalent experience. Proven expertise with LLMs, NLP, and machine learning is essential, alongside strong programming skills in Node.js and experience with RESTful APIs. Familiarity with vector databases and cloud environments is a plus, highlighting the need for a hands-on technical background.

At Broadvoice, we are committed to supporting the professional growth of our employees, including AI Engineers. We provide clear growth paths, access to a wide range of learning resources, and mentorship opportunities to help you advance your technical skills. Whether you're aiming to enhance your leadership capabilities or dive into new areas, we create an encouraging environment for your career journey.

The work culture for remote AI Engineers at Broadvoice is collaborative, inclusive, and supportive. Our team values diversity and a sense of belonging, fostering an environment where every voice is heard. You will work with talented colleagues who are passionate about innovation, making your contributions impactful while enjoying a healthy work-life balance.

As an AI Engineer at Broadvoice, you will work with advanced tools and technologies, including large language models like OpenAI, Anthropic, and more. Familiarity with vector databases like Pinecone, Weaviate, or FAISS, as well as cloud platforms such as AWS, Azure, or GCP, will be key to your success. You'll also be applying techniques like prompt engineering to refine AI models and implementing retrieval-augmented generation approaches.

When answering this question, detail specific projects or experiences where you've implemented or experimented with LLMs. Discuss the models you’ve used, the challenges you faced, and how you overcame them. Highlight any optimization techniques or innovations you contributed, demonstrating your depth of knowledge and hands-on experience.

It's important to showcase your teamwork and communication skills here. Discuss strategies you employ to ensure effective collaboration, such as regular meetings or utilizing collaborative tools. Provide examples of past experiences where your collaboration led to successful integration of AI features into a product.

Highlight your strategic approach to prompt engineering by giving examples of the methodologies you've implemented. Discuss any frameworks or testing you conduct to iteratively refine prompts, along with how you've measured model performance improvements as a result.

Share your commitment to continuous learning by mentioning specific resources you rely on, such as AI research journals, online courses, webinars, or communities. Discuss how this ongoing education influences your work and decision-making in your role.

When responding, frame your answer in the STAR format (Situation, Task, Action, Result). Describe the challenge, what your task was, the actions you took to resolve it, and the positive outcomes that followed. This structure clearly demonstrates your problem-solving abilities and impact.

Discuss specific cloud platforms you've worked with, like AWS, Azure, or GCP. Outline your approach to deploying models, including what challenges you faced and how you ensured scalability and reliability in your solutions.

Explain the metrics you use to evaluate model performance, such as accuracy, precision, or F1 score. Mention any validation techniques you employ, like cross-validation or A/B testing, and how they help you ensure your models meet desired outcomes.

Reflect on your passion for AI and its potential to solve problems and drive innovation. You could mention specific areas of interest, like enhancing user experiences or contributing to important projects, illustrating how these motivations inspire your approach to work.

Provide insightful observations on current trends, such as ethical AI, advancements in generative models, or the adoption of AI across various sectors. Share your thoughts on why these trends matter and how they may influence future developments in the field.

Personalize your response by mentioning Broadvoice's values, culture, or specific projects that excite you. Connect your career goals with Broadvoice’s mission and explain how you envision making a meaningful contribution to the team.