Support the development and deployment of machine learning and AI models across ALS business functions Assist in building AI solutions using Large Language Models (LLMs) Contribute to Agentic AI systems, including tool-using agents, orchestration workflows, and task automation Develop, train, test, and evaluate conventional machine learning and deep learning models Work with structured and unstructured data for model development, feature engineering, and data preparation Collaborate with senior AI engineers and data scientists to improve model performance, reliability, scalability, and maintainability Support cloud-based AI and ML development using Azure, Google Cloud Platform (GCP), and AWS Assist in building model pipelines, experimentation workflows, and deployment processes Participate in code reviews, technical documentation, testing, and quality assurance activities Stay current with emerging trends in machine learning, generative AI, LLMs, deep learning, and cloud AI services Follow ALS standards for security, privacy, data governance, and responsible AI practices Requirements

Hands-on experience with Google Cloud Platform (GCP) Experience using cloud AI and machine learning services, model hosting, or MLOps tools Familiarity with vector databases, embeddings, retrieval-augmented generation (RAG), or semantic search Experience with APIs, microservices, or integrating machine learning models into applications Knowledge of version control, testing, CI/CD, and documentation Exposure to containerization tools such as Docker Experience working with enterprise data environments or cross-functional technical teams Degree or diploma in Computer Science, Data Science, Software Engineering, Machine Learning, Artificial Intelligence, or a related technical discipline Practical experience with machine learning, including model training, evaluation,



and deployment concepts Experience with deep learning frameworks such as PyTorch or TensorFlow Exposure to Large Language Models (LLMs) Familiarity with Agentic AI concepts, including autonomous agents, tool calling, workflow orchestration, and AI assistants Programming experience in Python Familiarity with scikit-learn, pandas, NumPy, or similar libraries Experience with at least one major cloud platform, including Azure, Google Cloud Platform (GCP), or AWS Ability to work effectively in a collaborative, hybrid team environment Strong analytical thinking and problem-solving skills Willingness to learn Ability to sit at a desk and perform general office work for extended periods, with periodic computer/screen use Must be a citizen or permanent resident of the country applied for, or hold or be able to obtain a valid working visa Core Competencies

Demonstrates expertise in developing and deploying machine learning and AI models, with a robust focus on Large Language Models (LLMs) and cloud-based AI services. Proficient in collaborating with cross-functional teams to enhance model performance and adhere to security and governance standards. Highest-signal resume keywords

Machine Learning Model Development Large Language Models (LLMs) Google Cloud Platform (GCP) Deep Learning Frameworks (PyTorch, TensorFlow) MLOps Tools ATS Optimization Keywords

Hard Skills

Machine Learning Deep Learning Python Programming Feature Engineering Model Evaluation Data Preparation APIs Integration Containerization (Docker) Version Control CI/CD Soft Skills

Analytical Thinking Problem-Solving Collaboration Willingness to Learn Certifications & Qualifications

Degree in Computer Science Degree in Data Science Degree in Software Engineering Degree in Machine Learning Degree in Artificial Intelligence Industry Keywords

AI Solutions Task Automation Orchestration Workflows Data Governance Responsible AI Practices Tools & Technologies

Azure Google Cloud Platform (GCP) AWS Scikit-learn Pandas NumPy Vector Databases Retrieval-Augmented Generation (RAG) Microservices Agentic AI Concepts

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📌 Junior Machine Learning Engineer (North Vancouver)
🏢 Jobtailor
📍 North Vancouver