· Design and implement ML/AI solutions end-to-end, from the idea and data exploration phase to deployment and monitoring, balancing cutting-edge techniques with pragmatism to deliver measurable impact.
· Apply strong software engineering principles, such as modularity, testing, code reviews, CI/CD and observability, to ensure AI systems are reliable, maintainable, production-ready and can be readily adapted to future developments.
· Choose the right approach for the problem at hand, evaluating classical ML and NLP techniques, LLM-based solutions, and agentic solutions to balance trade-offs between speed, cost, complexity, interpretability, and performance.
· Collaborate closely with product, design, and other engineering teams to scope work, align on success metrics, and incrementally ship improvements in user-facing features powered by AI.
· Document system architectures and decision rationale early and clearly, enabling alignment across teams and accelerating onboarding and iteration.
· Champion model and data quality, including dataset versioning, robust evaluation, fairness/bias assessment, and real-world performance tracking.
· Mentor junior AI engineers and cross-functional teammates, sharing best practices in modelling, coding, maintaining and integrating product features, and helping grow a high-trust, high-performance team culture.
· Stay up-to-date with emerging research and tools, distilling key insights and bringing back relevant innovations to elevate team capabilities and product opportunities.
· Contribute to a culture of knowledge sharing, through company-wide Slack channels, Show and Tell presentations and technical deep-dives.