Senior ML/AI Engineer

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🕒 May 2

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BPO2B Global Inc

WebsiteLinkedInAll Job Openings

51 - 200 employees

People Performance Innovation

📋 Description

• 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.