Senior Applied AI Engineer (JS)

As a Senior Applied AI Engineer at Make, you will design and implement AI-based features and customer solutions, focusing on using Large Language Models (LLMs) and other AI technologies. You'll collaborate closely with product managers and AI/ML experts to build scalable, high-quality AI products that meet customer needs and streamline operational processes.

To qualify for the position of Senior Applied AI Engineer at Make, candidates typically need a Bachelor’s or Master’s degree in Computer Science or a related field, alongside 3-5+ years of practical experience. Strong communication skills, experience in AI/ML solution development, and a passion for technology are essential.

At Make, you can anticipate a dynamic and collaborative work environment that encourages innovation and creativity. The company fosters an inclusive culture where diverse ideas are celebrated, allowing you to work with a multinational team dedicated to transforming the automation landscape.

Make emphasizes professional growth, providing structured Learning & Development plans, including online courses, professional workshops, and attendance at conferences. Employees also benefit from a set allotment of learning days each year to help them pursue additional training.

Senior Applied AI Engineers at Make work with a variety of programming languages and tools. The tech stack includes Node.js, TypeScript, PostgreSQL, Redis, and Python for back-end development. Familiarity with LangChain, AI APIs, and Kubernetes is also valuable for their projects.

Make offers an attractive benefits package for Senior Applied AI Engineers, which includes RSU grants, annual bonuses, flexible working hours, ample vacation days, remote work allowances, health insurance, and perks such as a vibrant snack bar and regular team events.

Make is committed to creating an inclusive environment for all employees, valuing diverse perspectives and backgrounds. The company actively embraces diversity and follows affirmative action principles to ensure equal employment opportunities across all levels.

In your response, highlight specific projects where you've worked with LLMs. Discuss the model types you’ve used, the problems you aimed to solve, and any fine-tuning processes you undertook. Provide metrics to showcase the impact your work had on project outcomes.

Demonstrating your understanding of scalability is key. Discuss how you've designed architectures that accommodate growing data volumes while maintaining performance. Illustrate this with previous examples and solutions you've implemented in past positions.

Explain the various techniques you employ for evaluation, such as A/B testing, cross-validation, or performance metrics (e.g., accuracy, precision, recall). Share how these methods help you refine AI models and ensure they meet business objectives.

Convey your commitment to continuous learning by mentioning specific resources like industry journals, conferences, and online courses. Discuss how you've implemented learnings from new AI trends into your work, showcasing a proactive approach to professional development.

Provide a detailed anecdote about a difficult project, outlining the challenges faced, the steps taken to address them, and the eventual outcomes. Focus on your contribution and how collaborative efforts led to success.

Discuss the importance of API design for usability, performance, and security when serving AI models. Mention specifics such as documentation, version control, and how you ensure scalability and low-latency access for clients.

Explain your approach to receiving and acting on customer feedback. Discuss your methods for gathering this feedback, how you analyze it for actionable insights, and how you ensure that it informs future development decisions.

Articulate your strategies for developing robust data collection mechanisms while ensuring data quality and compliance with regulations. Provide examples of frameworks or tools you’ve implemented in previous roles.

Emphasize the significance of teamwork in AI development. Discuss how you work with product managers, cross-functional teams, and stakeholders to ensure that AI solutions address business needs and deliver value.

Clarify your expertise in developing end-to-end machine learning pipelines. Discuss the tools and frameworks utilized, the challenges faced, and how you ensured the seamless integration of data, modeling, and deployment within your projects.