Machine Learning Engineer

The Role You will work closely within a cross-platform, multi-disciplinary team in delivering leading mobility solutions for cities in the region. You will play a critical role in the conceptualisation, development, testing, and implementation of machine learning solutions and pipelines across Asia Mobiliti’s services and solutions. You must be able to understand programming languages and machine learning concepts that are required for engineering innovative solutions. Learn and grow in a startup environment with a passion for using technology to improve public transport and mobility in the developing world. The Activities Design, develop, and implement machine learning models for a variety of applications in the mobility sector (Prediction systems, recommendations systems, scoring systems, etc) Develop, integrate and maintain production-ready machine learning pipelines, with model evaluation and validation. Design and deliver user centric dashboards for effective visualisation of mobility metrics and other system metrics relevant to the user. Design, build, and maintain ETL (Extract, Transform, Load) processes for data ingestion, processing,and storage. Proactively identify and solve challenging problems related to data, models, and infrastructure Effectively communicate complex technical concepts to both technical and non-technical audiences. Academic Requirements: Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, or a related engineering field. 2 years of working experience in a role that primarily involves machine learning / deep learning. Fresh graduates are also encouraged to apply. Skills: Programming Proficiency: Programming skills in Python, including experience with relevant libraries such as TensorFlow, PyTorch, scikit-learn, Pandas, and NumPy Strong Mathematical and Statistical Foundation: Understanding of linear algebra, calculus, probability, and statistical inference. Machine Learning Expertise: Proficiency in various machine learning algorithms (e.g., regression, classification, clustering, deep learning), their strengths and weaknesses, and their appropriate applications Working Understanding of Large Language Models (LLMs): Familiarity with the architecture, capabilities, and limitations of transformer-based models (e.g., BERT, GPT, T5). Understanding of common LLM tasks (e.g., text generation, summarization, question answering) and basic techniques for interacting with and fine-tuning them. Cloud Computing: Experience or prior understanding of cloud platforms like AWS or GCP, particularly with their AI and machine learning services (e.g., Vertex AI, Azure Machine Learning). Data Engineering Skills: Experience with data warehousing concepts, ETL processes, and data pipeline development. Proficiency in SQL, understanding of noSQL and experience with data engineering tools and technologies (e.g., BigQuery, MongoDb). Problem-Solving Skills: Excellent analytical and problem-solving skills, with the ability to break down complex problems into smaller, manageable parts. Communication Skills: Strong communication skills, both written and verbal, with the ability to explain technical concepts clearly and concisely.