Backend Engineer

As a Backend Engineer, you'll primarily focus on developing and optimizing large language models and multi-agent AI systems to facilitate healthcare automation. This includes building Python-based microservices, deploying and managing ML services on Kubernetes and GCP, and utilizing frameworks like TensorFlow and PyTorch to create cutting-edge AI solutions.

To qualify for the Backend Engineer role with Weekday's client, candidates should possess proficiency in Python, with FastAPI experience being a plus. Strong expertise in machine learning, familiarity with LLMs, and hands-on experience with TensorFlow and PyTorch are essential. Additionally, experience deploying ML models using Kubernetes and GCP is crucial for this role.

A Backend Engineer at Weekday's client contributes to healthcare innovation by developing and optimizing AI solutions that automate healthcare processes. This involves conducting research to enhance model efficiency, collaborating with cross-functional teams, and integrating AI-powered solutions into real-world applications, ultimately improving patient outcomes and operational efficiency.

The work environment for a Backend Engineer at Weekday's client is fast-paced and research-driven. Employees can expect an atmosphere that encourages innovation and creative problem-solving, collaboration with talented professionals, and a commitment to improving healthcare through advanced technology.

For a Backend Engineer role at Weekday's client, proficiency in Python is crucial. Familiarity with FastAPI is advantageous, and experience with machine learning frameworks like TensorFlow and PyTorch will also be beneficial for the tasks involved in the role.

In previous projects, I utilized Python to develop robust applications, focusing on scalable microservices. I employed frameworks such as FastAPI to enhance performance and optimize efficiency, showcasing my ability to build high-quality back-end systems.

I have significant experience deploying machine learning models in Kubernetes. This involved setting up the necessary infrastructure, configuring pods, and utilizing Kubernetes services for monitoring and scaling applications effectively.

To ensure high performance and availability, I focus on optimizing ML models for production environments, using techniques like model pruning and quantization. Additionally, I implement load balancing and autoscaling capabilities within Kubernetes to manage traffic effectively.

I have hands-on experience with Google Cloud Platform, including deploying applications using Cloud Run and leveraging BigQuery for data analysis. This experience has allowed me to effectively manage cloud resources and integrate ML solutions into scalable back-end architectures.

I'm most familiar with TensorFlow and PyTorch, which I have used extensively to develop and fine-tune machine learning models. I appreciate their flexibility and the ease with which I can implement complex algorithms for AI solutions.

I stay updated with advancements in machine learning by attending workshops, participating in online courses, and following influential researchers in the field. Engaging in community forums and contributing to open-source projects also helps me stay abreast of emerging trends.

When optimizing ML models for production, I focus on reducing complexity, improving inference speed, and ensuring robustness. Techniques like hyperparameter tuning, feature selection, and using efficient data formats play a crucial role in this process.

The biggest challenges when integrating AI into healthcare applications include addressing data privacy concerns, ensuring model interpretability, and overcoming resistance to change from healthcare professionals. Collaborating closely with stakeholders to understand their needs is vital to mitigate these challenges.

I successfully collaborated with a cross-functional team on a project that required integrating AI solutions into a clinical decision support system. By actively communicating with data scientists, UX designers, and healthcare professionals, we were able to deliver a comprehensive product that met everyone’s expectations.

I'm interested in the Backend Engineer position at Weekday's client because it aligns with my passion for AI in healthcare. The opportunity to work on innovative solutions that can truly make a difference excites me, and I believe my skills in Python and machine learning can contribute to the team's success.