At Toku, we create bespoke cloud communications and customer engagement solutions to reimagine customer experiences for enterprises. We provide an end-to-end approach to help businesses overcome the complexity of digital transformation and deliver mission-critical CX through cloud communication solutions. Toku combines local strategic consulting expertise, bespoke technology, regional in-country infrastructure, connectivity, and global reach to serve the diverse needs of enterprises operating at scale. Headquartered in Singapore, Toku supports customers across APAC and beyond, with a growing footprint across global markets.
As an Applied AI Engineer at Toku, you will focus on building, improving, and deploying real-world AI capabilities across speech-to-text, chatbots, and large language model–driven features used in production. This role combines hands-on model development with applied research, where you will evaluate existing approaches, explore new techniques, and translate research insights into practical improvements in live systems. You will work closely with engineering teams to integrate models into production services while maintaining a strong delivery mindset. You will thrive in this role if you enjoy balancing deep technical execution with curiosity-driven, applied research that directly shapes product outcomes.
What you will be doing
Applied AI & Model Development:
Train, fine-tune, evaluate, and improve NLP, speech-to-text, and LLM-based models used in production environments
Work hands-on with chatbots, summarisation, and language understanding features, including retrieval-augmented generation (RAG) and vector-based retrieval systems
Design and run model evaluations, benchmarking existing approaches and validating improvements before deployment
Applied Research & Experimentation:
Read, assess, and experiment with relevant AI/ML research and emerging techniques, translating promising ideas into practical, production-ready solutions
Contribute to prompt design, model optimisation, and iterative experimentation to improve accuracy, latency, and reliability of deployed models
Production Integration & Delivery:
Integrate models into existing backend services using Python-based APIs, collaborating closely with backend engineers
Ensure models are production-ready, maintainable, and resilient when deployed in live customer-facing systems
Support investigation and resolution of AI-related production issues in collaboration with engineering and platform teams
Collaboration & Ownership:
Work closely with engineering teams to align AI capabilities with product requirements and platform constraints
Communicate progress, trade-offs, and technical decisions clearly in planning and delivery discussions
We’d love to hear from you if you have
Core AI & LLM Expertise:
Strong hands-on experience with LLMs, NLP, or speech technologies, including training, fine-tuning, and evaluating models in real-world or production contexts
Practical experience with Python-based AI development (e.g. PyTorch and related ecosystems)
Applied Research & Fundamentals:
Hands-on experience reading, evaluating, and applying AI/ML research (e.g. papers, benchmarks, emerging techniques) and translating those insights into production-ready model improvements
A strong foundation in AI/ML fundamentals (e.g. mathematics, machine learning concepts, model behaviour and evaluation), typically supported by an academic background in AI, machine learning, computer science, or a closely related field
Production & Integration Experience:
Experience deploying or supporting AI models in production systems, including exposure to monitoring, iteration, and real-world failure modes
Ability to integrate models into existing backend services via Python APIs and work effectively within a microservices-based environment
Tools & Platform Awareness:
Familiarity with retrieval-augmented generation (RAG), embeddings, and vector-based retrieval systems
Working knowledge of AWS-based environments and AI tooling (e.g. EC2, SageMaker, MLflow, Docker)
Ways of Working:
A proactive, problem-solving mindset with the ability to identify opportunities for improvement rather than waiting for direction
Strong collaboration and communication skills when working with engineers across different disciplines
What would you get?
Training and Development
Discretionary Yearly Bonus & Salary Review
Healthcare Coverage based on location
20 days Paid Annual Leave (15 days for Malaysia based roles), plus other leave allowances
Toku has been recognised as a LinkedIn Top Startup and by the Financial Times as one of APAC’s Top 500 High Growth Companies. If you’re looking to be part of a company on a strong growth trajectory while working on meaningful, real-world challenges, we’d love to hear from you.
