AI Backend Engineer

As an AI Backend Engineer at Tyme, your primary responsibilities include designing, developing, and maintaining backend services to support AI-driven banking applications. You'll integrate AI models into our microservices architecture, implement secure REST APIs and event-driven communication mechanisms, and utilize AWS services for developing serverless applications. Moreover, you're expected to monitor, troubleshoot, and optimize services to ensure they meet our high-performance standards while collaborating with cross-functional teams.

To qualify for the AI Backend Engineer position at Tyme, you should possess at least 4 years of experience in backend development, particularly with microservices using Java, Spring Boot, or Python (FastAPI preferred). A solid understanding of microservices architecture and cloud computing, particularly AWS services, is essential. Additionally, familiarity with AI and ML concepts as well as strong analytical and collaboration skills are vital.

In the role of AI Backend Engineer at Tyme, you'll work with cutting-edge technologies such as AWS Cloud, microservices architecture, Python, FastAPI, and tools for event-driven communication like Kafka and AWS Service Bus. Your work will also involve implementing REST APIs and developing serverless applications using AWS Lambda, ensuring you stay at the forefront of technology in the financial domain.

As an AI Backend Engineer at Tyme, you'll enjoy a competitive benefits package that includes meal and parking allowances, comprehensive health insurance, performance bonuses, and a pro-rata 13th-month salary. Additionally, you’ll have 15 days of annual leave plus sick leave, birthday leave, and Christmas leave. We also value your professional growth, offering clear career opportunities, SMART goals, and chances to participate in internal events and trainings.

At Tyme, we prioritize your career development by providing opportunities for continuous learning through technical seminars, conferences, and career discussions. Our company culture encourages participation in internal hackathons and team-building events, which not only enhances your skills but also fosters collaboration and innovation among team members. We believe in setting SMART goals to help guide your professional journey.

When designing a microservices architecture for an AI application, I start by clearly defining the business requirements and potential use cases. I ensure that each microservice encapsulates a specific functionality, promoting loose coupling and high cohesion. I'll then select appropriate communication protocols, such as REST APIs or messaging systems like Kafka, keeping scalability and maintainability in mind. The integration with AI models is crucial, so I consider their operational requirements from the outset.

In my previous roles, I have hands-on experience with various AWS services including Lambda for serverless applications, DynamoDB for NoSQL data storage, and API Gateway for managing APIs. I have leveraged CloudWatch for monitoring and performance optimization, ensuring the backend services are running efficiently and troubleshoot any issues that arise proactively.

Integrating AI models into backend services typically involves several steps. First, I ensure that the AI model is trained and tested for optimal performance. Next, I utilize REST APIs or messaging queues to facilitate smooth communication between the AI model and the backend service. I also focus on maintaining security and scalability throughout the process. Once integrated, I monitor the system's performance and make adjustments as needed based on user feedback and usage analytics.

To ensure scalability of backend services, I design them using a microservices architecture, which allows independent scaling of components based on demand. I also utilize cloud resources efficiently, employing auto-scaling features, and load balancers to distribute traffic. Additionally, conducting regular performance testing helps identify potential bottlenecks before they impact the system.

One of the most challenging technical issues I faced was when a newly integrated AI model was causing significant latency in our backend service. To resolve this, I conducted thorough performance profiling to identify bottlenecks in the service calls. I optimized the interaction between the AI model and the backend by implementing asynchronous processing and caching frequently accessed data, greatly improving response times.

I use a variety of monitoring tools like AWS CloudWatch and X-Ray to track the performance of backend services. I look for key metrics such as latency, error rates, and throughput. For optimization, I conduct regular code reviews and performance tests, refactoring code when necessary, and implementing caching strategies to improve efficiency.

I stay updated with the latest developments by following industry-leading blogs, participating in webinars, and being active in online communities. Joining professional networks and attending workshops and conferences also provides valuable insights and enables me to share knowledge with peers, enhancing my understanding of emerging technologies.

My experience with event-driven architectures primarily involves implementing systems where microservices communicate through events for better responsiveness and decoupling. I've worked with technologies like Kafka and AWS EventBridge that promote real-time data processing and scalability for applications, allowing services to react to events rather than relying solely on synchronous calls.

When juggling multiple projects, I prioritize tasks based on deadlines, business impact, and complexities involved. I utilize project management tools to track progress and communicate with team members regularly to align on priorities. Setting SMART goals for each project helps me focus my efforts and allocate resources efficiently.

Effective collaboration with cross-functional teams is vital in an Agile environment. I employ regular communication through scheduled stand-ups and use collaborative tools like Slack and Jira to keep everyone updated. Actively listening to team members' insights and feedback is crucial as well, allowing us to adapt quickly to any challenges that arise during the development process.