Machine Learning Engineer (LLM / NLP)

As a Machine Learning Engineer at Tiger Analytics, your primary responsibilities will include designing, developing, and implementing innovative machine learning models, particularly focusing on LLM and NLP applications. You will also be tasked with data preprocessing, feature engineering, and optimizing models for specific use cases. Given our partnerships with Fortune 500 companies, you will play a vital role in transforming complex data into actionable insights, making this position both impactful and rewarding.

To qualify for the Machine Learning Engineer position at Tiger Analytics, candidates should possess at least 5 to 6 years of total experience, with a minimum of 2 to 3 years specifically related to LLM and NLP skills. Proficiency in Python and familiarity with frameworks like TensorFlow and PyTorch are essential. Additionally, experience with NLP techniques, data preprocessing, and a solid understanding of software engineering principles will also be important to thrive in this role.

A Machine Learning Engineer at Tiger Analytics should be skilled in Python and well-versed in libraries and frameworks such as TensorFlow, PyTorch, and Hugging Face Transformers. Familiarity with tools used for data manipulation and model tuning, along with experience in AWS architecture and services, is also highly beneficial. Moreover, understanding Agile Development principles and using software engineering tools like Git are essential parts of the role.

Career growth for a Machine Learning Engineer at Tiger Analytics is promising and abundant, especially as we are a fast-growing and dynamic firm. Employees are encouraged to take initiative and lead projects, fostering an environment where you can significantly enhance your skill set. With opportunities for learning new technologies and tools, coupled with the chance to work on exciting analytics projects, you can look forward to a fulfilling career trajectory within our esteemed organization.

At Tiger Analytics, we strongly believe in the importance of continuous learning and development. We encourage our Machine Learning Engineers to pursue new knowledge and skills, facilitating access to advanced training, workshops, and resources that are essential for growth. Additionally, the collaborative environment here allows team members to learn from each other, share insights, and experiment with innovative technologies, all contributing to professional advancement.

In your response, share specific examples of projects where you've designed or implemented NLP models, detailing the types of tasks you performed, such as sentiment analysis or named entity recognition. Highlight any experience with LLMs like GPT or BERT, emphasizing how you optimized these models for particular applications.

Be sure to mention your proficiency in Python, as it's crucial for this role. Discuss your experiences with libraries like TensorFlow and PyTorch and how you have used them effectively in past projects. Don't forget to touch on any experience you have with AWS services if applicable.

Explain your systematic approach to data preprocessing, including techniques you use for cleaning, transforming, and preparing data for model training. Share specific examples and the impact effective preprocessing had on the final model's performance.

Detail the steps you take when fine-tuning LLM models, such as selecting the right architecture, choosing appropriate datasets, and evaluating model performance to ensure it meets the needs of the business case. Illustrating with examples will strengthen your answer.

Discuss your practices for writing maintainable code, including principles like modularity, documentation, and code reviews. Mention any specific tools you utilize, such as Git for version control, to maintain an efficient workflow.

Talk about your understanding of feature engineering and share some strategies you've employed in past projects. Highlight any specific techniques you've used, how they impacted model accuracy, and your overall approach to selecting and creating features.

Be candid about past challenges, such as data quality issues or integration difficulties. Emphasize how you identified solutions for these challenges, what you learned from the experience, and how it improved your subsequent projects.

Explain your methods for keeping up-to-date with the latest developments in machine learning, such as following influential publications, participating in online forums, attending conferences, and engaging with the data science community.

Provide an example of a project where collaboration was key to its success. Highlight your role within the team, how you communicated with others, and the importance of leveraging diverse skill sets to achieve your goals.

Discuss your experience with test-driven development and how it enhances the reliability of your machine learning applications. Mention specific techniques you've used and how they have helped in maintaining code quality throughout the lifecycle of your projects.