Senior ML/AI Engineer
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Job Description
Lead AI Engineer designing and deploying intelligent ML systems for travel personalization at a mobile travel app company. Collaborate with cross-functional teams to build advanced AI features.
Responsibilities:
- Lead the architecture, development, training, optimization, and deployment of AI/ML models and pipelines from prototype to production scale.
- Develop cloud-based AI services (leveraging LLMs and multimodal models via APIs like Gemini, OpenAI, or equivalents) for advanced personalization, recommendation engines, natural language understanding of travel queries, and feedback loops.
- Establish and enforce best practices: clean ML code, experiment tracking, MLOps (model versioning, monitoring, A/B testing, drift detection), automated evaluation pipelines, cost-efficient inference, and ethical AI considerations (bias mitigation, privacy).
- Collaborate on feature definition, data requirements, success metrics, and iteration.
- Handle unique challenges in an AI-powered travel app: multimodal data (location, text, images), sparse/seasonal travel datasets, real-time adaptability, offline/online hybrid inference, and seamless integration with mobile clients and backend APIs.
- Mentor future team members as we expand the AI engineering group and help shape our AI culture from the start.
Requirements:
- Professional AI/ML engineering experience building and shipping production AI systems for consumer mobile or web apps (personalization, recommendation, or travel-related experience is a plus).
- Deep hands-on experience with on-device ML: Core ML, TensorFlow Lite / LiteRT, ML Kit GenAI APIs, Gemini Nano integration, model optimization (quantization, pruning), and hybrid on-device/cloud architectures.
- Practical experience with cloud AI/LLMs: fine-tuning, prompt engineering, RAG, tool calling, API orchestration, and building feedback/improvement loops for personalization.
- Proven success owning 0-to-1 AI projects or leading major ML initiatives and comfortable making foundational decisions on model choice, data strategy, and deployment independently.
- Strong product sense: focus on user-centric metrics (engagement, conversion, satisfaction), low-latency inference, privacy-by-design, and robust evaluation in real-world scenarios.
- Excellent collaboration and communication skills and able to discuss AI trade-offs (accuracy vs. speed vs. cost) clearly with non-technical partners and engineers.
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