Applied ML Engineer

As an Applied ML Engineer at Kognitos, you will design, implement, and deploy machine learning models focused on agentic workflows, optimizing AI systems for multimodal applications, and innovating fine-tuning techniques for resource efficiency. You’ll also ensure that AI systems align with safety protocols and deliver consistent business value, collaborating with cross-functional teams to create impactful solutions.

To be considered for the Applied ML Engineer role at Kognitos, candidates should hold a Bachelor's, Master's, or Ph.D. in Computer Science, Machine Learning, or a related field, along with proven experience in developing and deploying machine learning models. Familiarity with AI safety principles, multimodal language models, and proficiency in frameworks like Python, TensorFlow, or PyTorch are essential.

As an Applied ML Engineer at Kognitos, you will be involved in exciting projects such as developing innovative methods for optimizing resource usage in training models, creating AI solutions that enhance enterprise automation, and advancing deterministic task execution workflows. You’ll also work on multimodal AI applications, combining text and images for robust automation tools.

Kognitos is fully committed to fostering an environment of professional growth for our Applied ML Engineers. By staying at the cutting edge of AI research, you'll have opportunities to incorporate new advancements into our platform. You will collaborate with a world-class team and engage in impactful projects in a dynamic, diverse workplace that values different backgrounds and skills.

The work culture at Kognitos for an Applied ML Engineer is collaborative, innovative, and inclusive. We pride ourselves on encouraging teamwork across various departments, sharing ideas, and exploring new solutions. Our commitment to diversity enhances our creativity and problem-solving abilities, making Kognitos a great place to work and grow.

When tackling fine-tuning, I analyze the model architecture's strengths and weaknesses, adjust hyperparameters for optimal performance, and implement techniques such as pruning or quantization to reduce resource consumption without sacrificing accuracy. Providing examples of previous experiences will strengthen your response.

AI safety and alignment ensure that AI operates reliably within the designed frameworks, delivering expected business outcomes without unintended consequences. Discuss your understanding of alignment strategies and practical experiences where you implemented safety measures in AI projects.

I have hands-on experience with multimodal language models, where I combined various data types, such as text and images, to build adaptive AI solutions. Highlight past projects where you successfully utilized these models to address enterprise challenges, which showcases your capability in this area.

In my previous role, I worked on a project that required collaboration with product and engineering stakeholders. I facilitated discussions to align project goals, shared technical insights, and adapted our solutions based on feedback. Providing specific outcomes from these collaborations will reinforce the effectiveness of your teamwork skills.

I am proficient in frameworks like TensorFlow and PyTorch. Discuss your project experiences using these frameworks, focusing on how you leveraged their features to deploy machine learning models efficiently and achieve project objectives effectively.

I regularly read AI research papers, follow industry-leading journals, and attend relevant conferences or webinars. Sharing specific examples of how you have applied insights from recent research to your work can demonstrate your proactive learning approach.

I worked on a project that developed a model improving operational efficiency by analyzing large datasets, which ultimately led to a 30% reduction in operational costs for the client. Be prepared to discuss the process, challenges faced, and outcomes in detail to showcase your contribution.

Challenges often include ensuring model performance under varying data distributions and managing resource constraints. I address these by implementing robust monitoring solutions and continuous learning approaches. Share your personal experiences to illustrate how you navigated these challenges effectively.

To optimize a model for production, I focus on hyperparameter tuning, reducing model complexity, and employing techniques like pruning. Explain any specific methodologies you've used successfully, as practical examples will strengthen your answer.

Agentic workflows involve implementing AI that can autonomously reason, plan, and execute tasks. My understanding includes ensuring reliability and minimizing runtime errors. Describe any projects related to this, demonstrating how you navigated complexities related to agentic systems.