558 | Senior ML/Backend Engineer

As a Senior ML/Backend Engineer at Intetics Inc, you will design, develop, and deploy complex machine learning systems. You’ll work cross-functionally to integrate ML functionalities into our product stack. Your responsibilities also include creating CI/CD pipelines, managing cloud infrastructure, and ensuring the reliability and scalability of our systems.

Candidates for the Senior ML/Backend Engineer position at Intetics Inc should have 4+ years of engineering experience focusing on machine learning applications. Proficiency in ML frameworks such as TensorFlow or PyTorch, experience with LLMs, backend frameworks like Django or Flask, and cloud management skills are essential.

In this role, you will work on an innovative AI marketplace project from scratch, making significant contributions as the first developer. Your focus will be on developing and deploying state-of-the-art ML techniques and solutions, which will have a substantial impact on the product’s functionality.

Yes! A background in data science can be particularly beneficial for the Senior ML/Backend Engineer role at Intetics Inc. Familiarity with data analysis, statistical modeling, and tools like Pandas or NumPy will enhance your ability to work effectively with ML algorithms and large datasets.

Candidates should be familiar with a variety of technologies, including ML frameworks like TensorFlow and PyTorch, cloud platforms such as AWS, GCP, or Azure, containerization tools like Docker and Kubernetes, and backend frameworks like Django or Flask. Knowledge of frontend technologies and RESTful APIs would also be a plus.

Discuss specific projects where you utilized these frameworks, highlighting the models you developed, how you trained them, and the outcomes achieved. Be sure to emphasize your understanding of the entire ML lifecycle—from model conception through deployment.

Start by explaining your approach to model validation and ensuring it meets performance benchmarks. Discuss CI/CD practices for automating deployments and tools like Docker or Kubernetes that help manage scalability and reliability in production environments.

Explain your experience with microservices architecture and how it aids in building scalable applications. Discuss specific instances where you've implemented solutions or used cloud services to efficiently manage resource allocation and load balancing.

Detail specific LLM projects you’ve worked on, focusing on how you integrated these models into applications. Discuss challenges faced, how you optimized performance, and the impact the models had on functionality.

Share examples of CI/CD workflows you’ve implemented. Explain the tools you used (like Jenkins or GitHub Actions), the benefits this brought to your development process, and any challenges you overcame.

Discuss your approach to integrating code assistants in your workflow to enhance productivity. Provide examples of how you use them to automate routine coding tasks while ensuring that your code maintains high quality.

Describe your recent experiences working with product managers, frontend developers, and designers. Highlight the importance of clear communication and defining shared goals to deliver effective, integrated ML solutions.

Provide insights into specific challenges, such as data quality issues or performance bottlenecks. Talk about the analytical approaches or innovative solutions you implemented to overcome these hurdles and the results achieved.

Share your strategies for keeping knowledge fresh, such as attending conferences, following relevant publications, or participating in online courses. Emphasize continuous learning in the fast-evolving tech landscape.

Express your vision for how AI will drive future innovations in backend development, citing examples of emerging technologies and practices. Discuss the potential benefits and challenges that may arise in this transformation.