AI/ML Senior Software Engineer, United Kingdom

As an AI/ML Senior Software Engineer at BJAK, you will collaborate with product managers to define project objectives, design and implement AI models and algorithms, and manage the end-to-end AI model lifecycle. Your responsibilities will also include staying updated with AI advancements and driving data-driven decision-making through analytics.

To qualify for the AI/ML Senior Software Engineer role at BJAK, candidates should hold a Bachelor’s, Master’s, or Ph.D. in Computer Science, Artificial Intelligence, or a related field. Proven experience in AI engineering or data science along with proficiency in programming languages such as Python and frameworks like TensorFlow is also essential.

Absolutely! At BJAK, our flat organizational structure promotes ownership and initiative. If you consistently deliver outstanding results, there are ample opportunities for leadership roles and career advancement, supporting your professional growth in this exciting field.

You will work with a variety of AI and machine learning frameworks and tools, including but not limited to Python, TensorFlow, and advanced analytics techniques. Staying current with the latest technologies is encouraged, ensuring that you’re always at the cutting edge of the industry.

The work environment at BJAK is international and collaborative, fostering a culture of flat hierarchy that values contributions from all team members. Your roles will include ample opportunities for hands-on work and addressing unique, complex problems while being supported by a motivated and passionate team.

Certainly! When answering this question, focus on specific AI projects you've worked on, detailing your role and the technologies employed such as TensorFlow or Python. Highlight challenges faced and successes achieved, showcasing your problem-solving skills and expertise.

Discuss the importance of data cleaning and feature engineering in your process. Explain how you identify relevant features, handle missing data, and prepare datasets for model training, emphasizing your methodical approach towards ensuring data quality.

You might mention metrics such as accuracy, precision, recall, and F1 score. Sharing examples of past models and how you evaluated their performance will demonstrate your understanding of model evaluation techniques and their implications in real-world applications.

Highlight a specific instance where you faced a significant challenge, detailing the problem, your approach to finding a solution, and the outcome. Illustrating your critical thinking and problem-solving skills is essential here.

You can discuss techniques such as data encryption, access controls, and ensuring compliance with data protection regulations. Mention your proactive approach to mitigating risks associated with deploying AI solutions in sensitive environments.

Share your methods for staying updated, like following reputable AI research journals, attending conferences, or participating in online courses. This shows your commitment to continuous learning and staying ahead in the fast-evolving AI field.

Discuss the importance of collaboration, effective communication, and taking initiative. Convey how you have previously thrived in such environments, showcasing teamwork and your contribution to a collective goal.

Emphasize your analytical skills and the techniques you use to derive insights from data. Share examples of how decision-making was improved through data analysis in your previous work to support your answer.

Outline a specific situation where you assumed leadership roles, detailing how you organized the project, coordinated team efforts, and ensured successful delivery. Focus on your leadership, communication, and problem-solving skills.

Demonstrate your ability to simplify complex ideas by using analogies and relatable examples. Focus on the importance of clear communication and ensuring that key stakeholders are informed and engaged with the project.