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

#J-18808-Ljbffr

📌 Junior Machine Learning Engineer (British Columbia)
🏢 Jobtailor
📍 British Columbia