Senior ML/AI Engineer (Colombia)

 Posted a month ago
     
5-10 years experience
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AI Summary

Lead the architecture, development, and deployment of AI/ML models from prototype to production scale. Collaborate with cross-functional teams to integrate intelligent features like recommendations and travel assistance into mobile and cloud platforms.

As our lead Senior AI Engineer, you'll take full ownership of the AI and ML capabilities that make our product truly intelligent and hyper-personalized. You'll design, build, and deploy the core AI systems from the ground up, spanning on-device inference for responsive mobile experiences and cloud-based models for deeper personalization and real-time insights. Working closely with product, design, mobile (iOS/Android), and backend teammates, you'll translate high-level travel vision into production-grade AI features like intelligent recommendations, context-aware suggestions, adaptive itineraries, and anticipatory travel assistance. Expect to balance rapid prototyping with scalable, reliable, and efficient AI systems that respect privacy, battery life, and real-world travel variability.This is ideal if you enjoy greenfield AI projects, making foundational modeling and integration decisions in ambiguity, and shipping consumer-facing AI where model quality, latency, and user experience directly define the product.


Key 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.


Required Qualifications
- 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.


Preferred Qualifications
- Experience with travel/mobility domains (geospatial ML, recommendation systems for itineraries/routes, multimodal travel data, real-time context).
- Familiarity with MLOps tools (MLflow, Weights & Biases, Kubeflow equivalents), vector databases, or agentic workflows.
- Exposure to cross-platform AI consistency (iOS/Android alignment) or generative features in consumer apps.
- Open-source contributions, personal AI projects, or a portfolio demonstrating shipped ML models/systems (especially on-device or personalization-focused).

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