Responsibilities
- Own ML and GenAI systems end-to-end in production, including problem framing, system design, deployment, monitoring, and continuous improvement
- Make sound architectural and methodological decisions for ML systems on AWS, balancing robustness, observability, cost, and long-term maintainability
- Partner closely with Product, Engineering, and Business leaders to turn ambiguous problems into ML-enabled product solutions with clear outcomes
- Operate and evolve ML systems deployed across multiple countries and markets, accounting for differences in data distributions, regulations, and constraints
- Mentor other data scientists through hands-on technical guidance, design reviews, and shared ownership of systems
Requirements
- Advanced degree in Computer Science, Engineering, Mathematics, or a comparable field
- 5+ years of experience building and owning production ML systems end-to-end in a real-world environment
- Strong hands-on expertise in Python and modern ML frameworks, with the ability to ship, operate, and evolve ML systems in production
- Solid experience with AWS and MLOps practices, including deployment, monitoring, and operating ML systems at scale
- Practical experience applying GenAI / LLMs in applied or production contexts, with a clear understanding of trade-offs and limitations
- Ability to influence technical decisions through clear judgment and collaboration, not formal authority
- Experience supporting and guiding more junior data scientists through hands-on collaboration, code reviews, and technical feedback
- Strong sense of ownership and accountability for outcomes, with a pragmatic approach to trade-offs and delivery
