AI/ML Senior Software Engineer (Singapore)

As an AI/ML Senior Software Engineer at Bjak, you will engage with product managers and stakeholders to establish AI and machine learning objectives related to our innovative financial products. Your primary tasks will involve designing, developing, and implementing AI models and algorithms that address complex business problems. Overseeing the complete AI model lifecycle, from data collection to deployment, will be a significant part of your role, ensuring our systems are optimized for scalability and efficiency.

To be considered for the AI/ML Senior Software Engineer position at Bjak, candidates should hold a Bachelor’s, Master’s, or Ph.D. in fields such as Computer Science, Artificial Intelligence, or Data Science. Proven experience as an AI/ML Engineer, Senior Software Engineer, or Data Scientist is critical, along with proficiency in frameworks like Python, TensorFlow, and PyTorch. Strong analytical skills, effective communication abilities, and a collaborative spirit are essential for success in this role.

Yes! At Bjak, we offer flexible work arrangements for the AI/ML Senior Software Engineer position. Candidates who are Singaporean citizens or Permanent Residents have the option to work remotely from Singapore or can choose to relocate to Malaysia for a hybrid working setup. This flexibility allows you to balance your professional and personal life effectively.

Bjak embraces an exciting and fast-paced environment that values innovation and collaboration. As an AI/ML Senior Software Engineer, you will have the opportunity to work on cutting-edge projects that help shape the future of financial services in ASEAN. We prioritize learning and career growth, offering competitive salaries, performance incentives, and a flat organizational structure that encourages open communication and idea sharing.

A successful AI/ML Senior Software Engineer at Bjak should possess strong technical skills in AI and machine learning frameworks such as TensorFlow, PyTorch, and Scikit-learn. Proficiency in programming languages like Python is essential, along with expertise in data preprocessing, feature engineering, and model evaluation. Candidates should have a solid track record of deploying and optimizing AI models in production environments to effectively contribute to our innovative solutions.

When answering this question, emphasize your hands-on experience with specific frameworks like TensorFlow, PyTorch, or Scikit-learn. Share examples of projects where you successfully applied these tools to solve business problems, detailing the approach you took, challenges you faced, and the outcomes achieved.

Discuss the strategies you've employed to maintain scalability and security in AI models. This could include implementing robust data pipelines, using cloud services, optimizing algorithms for performance, and applying security best practices to protect sensitive information during model training and deployment.

Highlight your approach to data preprocessing and feature engineering, mentioning specific techniques such as normalization, handling missing values, and dimensionality reduction. Share examples of how these processes have impacted model performance positively in your projects.

Talk about the resources you utilize for keeping up with trends and advancements in AI and ML. Mention following influential thought leaders, participating in forums, attending workshops or webinars, and engaging with research papers to ensure you incorporate best practices in your work.

Be specific about a project where you identified a complex challenge and how you utilized AI/ML technologies to address it. Discuss the methods used, the data available, and the results achieved, emphasizing your problem-solving skills and the value added to the business.

Explain the evaluation techniques you apply, such as cross-validation, accuracy metrics, precision, recall, and F1 score. Provide specific examples of how you determined the effectiveness of your models and how you iteratively improved them based on evaluation results.

Outline your process for debugging AI models, which may include checking data integrity, reviewing logs, conducting error analysis, and testing various components of the model to pinpoint issues. Use examples from your experience where you identified and resolved problems effectively.

Discuss the importance of collaboration in your work, mentioning examples of how you’ve partnered with product managers, software engineers, or data scientists to enhance project outcomes. Highlight your communication skills and how you ensure everyone is aligned on the project's objectives.

Share a specific instance where you recommended an AI/ML strategy or technology. Focus on how you justified your decision, the impact it had on the project, and how you gained buy-in from stakeholders, demonstrating your persuasive communication and analytical reasoning.

Explain the techniques you use to monitor and optimize AI models after deployment. This could include continuous learning, feedback loops, retraining models on new data, and performance tuning to ensure they remain accurate and effective in changing environments.