ML Tech Lead, GenAI

As the ML Tech Lead at Provectus, you'll oversee a team of engineers, manage machine learning projects, and drive the development of innovative AI solutions. Your responsibilities include leading cross-functional teams, optimizing model performance, and implementing MLOps practices throughout the ML lifecycle to ensure a streamlined production environment.

To become an ML Tech Lead at Provectus, candidates should have proven experience with Large Language Models and NLP frameworks, strong expertise in AWS Cloud Services, and at least two years in a leadership role within the ML/AI domain. Additionally, hands-on experience with Python and various ML frameworks is essential.

At Provectus, we believe in fostering a culture of continuous learning and professional growth. As an ML Tech Lead, you will be responsible for conducting regular code reviews, facilitating mentorship, and providing ongoing feedback to your team, ensuring they develop the necessary skills to excel in their roles.

An ML Tech Lead at Provectus should possess strong skills in building and optimizing machine learning models using AWS services. You'll need in-depth knowledge of LLMs, NLP, deep learning, and classical machine learning techniques, alongside practical experience with tools like TensorFlow or PyTorch.

The ML Tech Lead at Provectus will work extensively with AWS services such as SageMaker, Lambda, and Bedrock for constructing scalable ML solutions. Familiarity with MLOps tools and a solid understanding of variation techniques like model optimization and debugging are also essential.

When answering this question, highlight specific projects where you implemented LLMs, mentioning the challenges faced and techniques used. Discuss your familiarity with frameworks like Hugging Face or OpenAI, and how these experiences have equipped you for the ML Tech Lead position at Provectus.

Emphasize your leadership style by discussing how you encourage collaboration and communication. Share examples of how you set clear expectations, provide constructive feedback, and facilitate team growth, showcasing your ability to maintain productivity and morale under pressure.

Discuss your approach towards MLOps practices, including CI/CD methods. Give examples of how you have optimized model performance during deployment and detail your experiences with troubleshooting and debugging to ensure smooth transitions from development to production.

Choose a specific instance that demonstrates your analytical and problem-solving skills. Describe the context, the steps you took to address the problem, and the outcomes, showing how these skills align with the requirements of an ML Tech Lead at Provectus.

Mention various methods you utilize to keep abreast of industry developments, such as following research papers, participating in forums, attending workshops, or webinars. This shows your commitment to professional development and awareness of emerging technologies relevant to your role.

Discuss specific AWS tools you have worked with, detailing projects where you architected cloud solutions for ML applications. Emphasize how you effectively utilized AWS services to enhance performance, scalability, and cost-efficiency in model deployment.

Explain your criteria for success, including performance metrics and business outcomes. Share instances where you made data-driven decisions based on project evaluations, highlighting your analytical skills and understanding of ML's business impact.

Outline your strategies for fostering a supportive learning environment, such as regular feedback sessions, training opportunities, and mentorship programs. Share specific examples where your efforts led to improved skills and knowledge within the team.

Discuss specific MLOps tools you've used, explaining how they facilitated efficient workflows in deploying and monitoring ML models. Highlight your understanding of best practices in model lifecycle management relevant to the expectations of the ML Tech Lead role.

Describe your approach to model optimization by mentioning techniques like hyperparameter tuning, feature selection, or performance evaluation. Provide insights into how these methods have improved the results in your previous projects, especially under the lens of the role at Provectus.