Applied AI Engineer – LLM, NLP

Applied AI Engineer focusing on building AI capabilities in speech-to-text and chatbots for enterprise customer engagement solutions at Toku.

Posted 7/27/2026full-timeRemote • 🇳🇱 NetherlandsMid-LevelSeniorWebsite

Tools & technologies

AWSDockerEC2MicroservicesPythonPyTorch

Key responsibilities & impact

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
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
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
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

What you’ll need

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)
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
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
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)
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.

Comp & perks

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