Applied AI Engineer – LLM, NLP

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Toku

WebsiteLinkedInAll Job Openings

51 - 200 employees

Founded 2018

📡 Telecommunications

☁️ SaaS

🤝 B2B

💰 $5M Series A - Toku on 2024-11

Telecommunications • SaaS • B2B

Toku is a cloud-based communications and customer experience platform that provides AI-powered conversational and telephony solutions focused on APAC languages and accents. The company offers business telephony integrations (Toku for Microsoft Teams, Toku for Zoom Phone), an AI-powered contact centre, AI voice agents, programmable voice and messaging embeddables, campaign and feedback management tools, and APIs for building in-app calling and messaging. Toku targets enterprises across industries (government, fintech, insurance, travel & hospitality, retail/e-commerce, sharing economy) with use cases like number masking and user verification to improve security and CX.

📋 Description

• 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

🎯 Requirements

• 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.

🏖️ Benefits

• 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