Senior AI/ML Engineer - R01544842

As a Senior AI/ML Engineer at Brillio, you will be responsible for developing machine learning models and algorithms to tackle various business challenges. You’ll engage in model deployment and integration into production environments while optimizing performance across scalability and efficiency. Additionally, monitoring and maintaining model performance, collaborating with cross-functional teams, and staying abreast of advancements in AI/ML are crucial aspects of your role.

To qualify for the Senior AI/ML Engineer position at Brillio, candidates typically need 8+ years of experience in machine learning and data science. A deep understanding of techniques such as supervised and unsupervised learning, proficiency in programming languages like Python or PySpark, and familiarity with various ML frameworks like TensorFlow and PyTorch are essential.

In the Senior AI/ML Engineer role at Brillio, you will work on a variety of cutting-edge projects that apply machine learning techniques to solve pressing business problems. This includes developing models that enhance automated processes, improve customer experiences, and optimize operational efficiencies across diverse industries. Your contributions will directly impact Brillio's mission of driving digital transformation for our clients.

Yes, the Senior AI/ML Engineer position at Brillio offers remote work flexibility, allowing you to perform your duties from San Ramon, California. This arrangement is designed to adapt to the modern work environment and provide a balanced approach to productivity and personal life.

Brillio is recognized as a Great Place to Work® and attracts top talent due to its commitment to innovation and employee development. As a Senior AI/ML Engineer, you will have access to a collaborative culture, opportunities for professional growth, and the chance to work on meaningful projects. Brillio's emphasis on contemporary technologies ensures you will constantly be learning and adapting in this fast-paced field.

In your answer, highlight specific projects where you utilized TensorFlow or PyTorch, detailing the challenges faced and how you solved them. Discuss your comfort level with building, training, and deploying models using these frameworks, emphasizing any performance optimizations you implemented.

Discuss your approach to model monitoring, including using metrics to evaluate accuracy and performance over time. Mention any tools or frameworks you have used to set up alerts for model drift or degradation and how you determine when retraining is necessary.

Share how you engage with the AI/ML community. This could include attending conferences, participating in online forums, subscribing to relevant journals, and continuously learning through online courses. Mention how you apply this knowledge to enhance your work as an AI/ML Engineer.

Provide a concise overview of a specific project, focusing on the problem it addressed, the solution you implemented, and the impact it had on the business. Highlight your role, the technologies used, and any quantitative metrics that demonstrate success.

Explain your process for data cleaning, transformation, and feature selection. Discuss the importance of these steps and how they impact model performance. You may also mention any tools or techniques you prefer for data preprocessing.

Discuss specific challenges you've encountered during deployment, such as integration with existing systems or scalability issues, and how you overcame them. Provide examples of how you collaborated with other teams to ensure successful deployment.

Talk about common optimization techniques you employ, such as hyperparameter tuning, cross-validation, and feature engineering. Provide examples of when you've successfully optimized a model and the results that followed.

Discuss strategies that you use to validate and test models before deployment, including using holdout sets, K-fold cross-validation, and A/B testing to ensure robustness and reliability. Highlight the importance of continual monitoring post-deployment.

Provide examples of how you have collaborated effectively with data scientists, software engineers, and product managers. Discuss communication strategies and how you ensure that the team aligns with project objectives.

Explain how you use hypothesis testing to evaluate model effectiveness or feature importance. Provide examples of statistical tests you've used and how their results influenced your decisions in model development.