AI/ML Engineer (Conversational AI)

AI/ML Engineer (Conversational AI)

About Zeals

Zeals is on a mission to “Design Conversations. Drive Conversions.”
We empower enterprises to deliver immersive shopping and customer-service experiences on popular messaging platforms through human centred AI. More than 480 Japanese brands already trust Zeals, and we recently secured a USD 33.8 million Series E round as we accelerate our global expansion.

Position Overview

As an AI/ML Engineer at Zeals you will design, build and optimise the machine-learning models and underlying infrastructure that power our Conversational Commerce platform. Your remit spans cutting edge Large Language Models (LLMs), Retrieval Augmented Generation (RAG) pipelines and vector search, as well as classical supervised / unsupervised ML algorithms for tasks such as intent classification, recommendation and user behaviour prediction. You will own the entire model lifecycle, data collection, feature engineering, training, evaluation, deployment and online monitoring, and work closely with product, backend and data teams to convert state of the art research into reliable, scalable and secure production systems that keep our chatbots smarter, safer and more natural.

What You’ll Do

• AI / ML & NLP Research & Optimisation
• Design, train and maintain LLMs, RAG pipelines, vector-retrieval modules, and classical ML models (e.g. intent classification, recommendation, anomaly detection).
• Build multilingual NLP components such as named-entity recognition (NER), sentiment analysis and topic modelling for Japanese, Chinese and English.
• Apply prompt engineering, fine-tuning, quantisation and distillation to maximise accuracy, latency and cost-efficiency.
• Own the full model lifecycle—data collection, feature engineering, training, evaluation and iterative improvement.
  
  
• Production Deployment & Continuous Delivery
• Partner with backend, data-engineering and product teams to deploy models on AWS / GCP with CI/CD, blue-green or canary releases, online monitoring and A/B testing.
• Build horizontally scalable training, inference and monitoring architectures (Docker / Kubernetes / serverless) to ensure high availability and low latency.
• Orchestrate end-to-end ML pipelines with Airflow, Kubeflow or similar, and maintain a model registry & feature store.
  
  
• Research & Technology Scouting
• Track cutting-edge AI/ML/NLP papers and open-source projects; quickly prototype, benchmark and translate promising ideas into product value.
• Conduct offline and online experiments, reporting metrics such as precision@k, recall, latency and serving cost to guide roadmap decisions.
  
  
• Internal Tooling & Knowledge Sharing
• Build automated experimentation platforms, reproducible notebooks and evaluation dashboards.
• Author best-practice documentation, code samples and tech talks to uplift team skills and drive a culture of continuous learning.

Must Have Qualifications

• 3+ years of hands-on experience shipping AI/ML/NLP products, covering the full lifecycle from POC to production.
• Proven expertise in RAG system design and implementation, integrating vector databases (FAISS, Pinecone, Qdrant, etc.) with LLM generators.
• Strong Python skills plus deep proficiency with PyTorch or TensorFlow.
• Practical experience with cloud deployment and containerisation (Docker/Kubernetes).
• Solid data-engineering fundamentals—designing efficient ETL pipelines, feature stores and data-quality controls.
• Excellent technical communication in English; Japanese fluency is a plus.

Nice to Have Qualifications

• End to end MLOps experience: model versioning, ML-centric CI/CD, feature stores, monitoring and automated retraining.
• Track record optimising large-scale LLM inference for speed or cost (quantisation, distillation, LoRA, etc.).
• Background in conversational AI, voice interfaces or multilingual chatbots.
• Hands-on experience with privacy (PII protection), generative-AI risk management or security hardening.
• Open-source contributions, peer-reviewed publications or an active technical blog.
• Familiarity with AWS Bedrock / SageMaker, GCP Vertex AI, serverless architectures or GPU-cluster cost optimisation.

Don’t just take our word for it 

Zeals has received the LinkedIn Top Startups award twice in a row. We believe this is a testament to our growth and our ability to attract great talents–who could potentially be your colleague! 

Ready to roll up our sleeves

• Salary Range : competitive
• Performance review: twice a year
• Holidays: 10 days of annual leave in the first year, weekends off, national holidays, summer and winter breaks, and 5 refreshment leave days per year
• Visa support: We sponsor visas for the right candidates
• Flexible working: Highly flexible, remote-first organization (in Japan), Interim work from overseas
• Housing allowance (within 1.5KM away from office)
• Club activity allowance
• Team building allowance and Lunch allowance
• Zeals Bar (bi-monthly free flow beer party)