Senior Backend Engineer, AI Solutions Engineer

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Requirements:

• At least 5 years of web development experience (Python, Java, or Kotlin preferred)
• Proficiency in SQL database design and interaction
• Knowledge of RESTful API principles and conventions
• Bachelor's degree in Computer Science or a related field, or equivalent experience
• Passion for building innovative, scalable solutions that have real customer impact
• Ability to work well on a highly collaborative team with other product development roles
• Ability to communicate well with others to promote ideas while showing mutual respect for others’ opinions
• Strong interest in AI technologies and a passion for exploring their potential to improve user experiences.
• Experience working with various AI tools, libraries, and platforms.
• A basic understanding of LLMs, their capabilities, and weaknesses.

Responsibilities:

• Work with your product manager, UX designer, engineers, and customers to define and validate new features and enhancements.
• Meet with frontend engineers to design effective REST API endpoints and document the service contract.
• Implement microservice endpoints using Python with asyncio or Java with Vert.x, PostgreSQL, Redis, RabbitMQ, and anything else you need to get the job done.
• Write unit tests for your code and participate in code reviews to ensure quality and best practices in other developers' code.
• Own service builds and deployments to our staging and production environments using Docker, GitHub, and Kubernetes.
• Identify, prioritize, and resolve bugs and technical debt.
• Mentor other developers on your team and share gained knowledge with the rest of the backend developers through trainings.
• Participate in interviews of candidates to help us continue to hire the best.
• Collaborate with the AI group to brainstorm and design AI-driven solutions that enhance the capabilities of our platform.
• Work on projects that leverage AI models and tools, ensuring seamless integration into the backend systems.
• Design, measure, and monitor business metrics that quantify AI model performance.
• Drive best practices for adopting AI technologies across development teams.