Job Summary
You will be responsible for implementing state-of-the-art AI technologies and developing enterprise-grade deep learning pipelines. Your focus will include creating robust speech-to-text and text-to-speech models tailored to Malaysian and Arabic languages and dialects. You will collaborate closely with cross-functional teams to ensure smooth model development, deployment, and real-time performance optimization, all contributing directly to the company’s AI-driven capabilities and business performance.
Key Responsibilities
- System Architecture: Design comprehensive system architectures integrating AI modules, ensuring robust communication between model components.
- Data Pipeline Management: Oversee the collection, cleaning, preprocessing, and normalization of large datasets to ensure high-quality inputs for model training.
- Model Design and Training: Develop and optimize neural network architectures using frameworks such as PyTorch to meet performance benchmarks.
- Model Evaluation and Fine-Tuning: Evaluate AI models using standard performance metrics, and conduct iterative fine-tuning to enhance accuracy and generalization.
- Model Deployment & Monitoring: Deploy AI models in production environments, implement performance monitoring, and optimize for real-time or streaming use cases.
- Security Measures Implementation: Apply secure development practices to protect sensitive data and ensure compliance in AI-related workflows.
- Research & Development: Stay current on advancements in AI, NLP, and LLMs; experiment with new technologies and implement innovative solutions where applicable.
- Custom AI Model Training: Train and fine-tune domain-specific large language models (LLMs) using local or proprietary datasets for speech and text applications.
- Infrastructure Setup & Maintenance: Design and maintain infrastructure for scalable model training and deployment — including servers, GPUs, and cloud-based resources.
- Optimization & Efficiency: Implement solutions for low-latency, scalable, and cost-effective AI systems across both batch and real-time pipelines.
- Cross-functional Collaboration: Partner with software engineers, product managers, and stakeholders to align AI functionality with product and business needs.
- Documentation: Maintain clear and comprehensive documentation covering model specifications, training methodology, evaluation procedures, and operational guidelines.
General Qualifications
- Bachelor’s degree or higher in Artificial Intelligence, Data Science, Computer Science, or a related technical field.
- Solid foundation in machine learning, deep learning, and natural language processing (NLP).
Experience and Skills
Technical Skills:
- Hands-on experience with PyTorch and deep learning model development.
- Proficiency in core NLP tasks — such as tokenization, text classification, sentiment analysis, etc.
- Proven track record of fine-tuning large language models (LLMs) such as GPT, LLaMA, etc.
- Strong Python development skills; comfortable working in Jupyter Notebooks, with version control (e.g., Git), and cloud tools (AWS preferred).
- Experience working with large-scale datasets using tools such as Apache Spark, Hadoop, or equivalent.
- Deployment experience using Docker, Kubernetes, and other container orchestration tools.
- Practical knowledge of real-time streaming and latency optimization strategies.
- Familiarity with MLOps tools and practices for scalable, automated workflows.
Soft Skills:
- Strong problem-solving and critical-thinking skills.
- Ability to clearly communicate complex technical concepts to both technical and non-technical audiences.
- Self-motivated and adaptable with a strong willingness to learn and grow in a fast-paced environment.
Preferred Experience
- Familiarity with Malaysian or Arabic languages and dialects to support culturally relevant language model training.
- Experience working with MLOps or production-scale LLM deployments.
- Background in AI research, academic contributions, or work in AI-focused environments.
Work Environment
Office-based role, with flexibility for hybrid or remote work, depending on company policy and team requirements.
Reporting Line
This position reports directly to the Chief Technology Officer (CTO).
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