- Leading development efforts, mentoring engineers and product managers, and making key architectural decisions that involve Data Science, Machine Learning and AI.
- Partner with Sales, Marketing, Customer Support and other departments across the organization for a full end to end ownership from the product and technical perspective as well as internal enablement and customer support.
- Develop greenfield projects and implement proof of concepts, including hands-on coding and connection to the product vision.
- Architect end-to-end AI systems by choosing the best AI approach (Data Science, Statistics, Machine Learning and Generative AI) for the problem to be solved, considering all relevant trade-offs and risks, and work hands-on directly in the code with other engineers.
- Bridge the gap between data science and product by translating applicable complex problems into holistic AI solutions, including the UX and UI, and leading its development while also managing from the product management perspective.
- Use data-driven solutions to address complex cybersecurity problems.
- Be responsible for the pipeline metrics and work hands-on with MLOps to optimize model and pipeline performance through proper monitoring, data mining, fine-tuning, prompt engineering, and hyperparameter tuning to meet latency and cost requirements.
- Drive technical strategy by evaluating third-party AI tools versus building in-house solutions to maximize ROI.
- Establish data governance and security standards to ensure the ethical and compliant use of sensitive information within AI models.
- Be able to create and implement incremental development and implementation plans based on a product long term vision.
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