Job Description Insight Global is looking for a Software Engineer (AI Platform) to join an enterprise AAA game company hybrid out of Vancouver BC on a permanent basis. The Infrastructure and Platform Services team serves as the backbone of the company’s global ecosystem, supporting the creation of exceptional games and immersive player experiences. As a part of the team, you will contribute to essential platforms such as Cloud, Commerce, AI, Gameplay Services, Identity, and Social. The AI Platform team delivers centralized AI resources across all the company's game franchises, crafting AI and Generative AI solutions alongside a shared AI infrastructure for company-wide applications. The team employs a state-of-the-art, cloud-based tech stack equipped with top-tier tools to support initiatives such as data modeling, model training and fine-tuning, and agent development. They provide solutions and platforms that empower the future of game development, marketing, sales, and player experiences. As a Software Engineer with expertise in AI/ML systems and platform development, you will help lead the creation of a scalable AI Platform. Key responsibilities include but are not limited to: Contribute to the design and development of core AI platform components to support machine learning lifecycle workflows (data and metadata ingestion, storage and indexing, model training, validation, deployment) within a live-service gaming environment Implement and maintain cloud-based infrastructure (on AWS, GCP or Azure) supporting scalable ML workloads, ensuring reliability, availability and cost-efficiency for game operations Assist in automating end-to-end AI workflows: build CI/CD pipelines for model deployment, containerized micro-services (Docker/Kubernetes), and metric instrumentation for model performance and monitoring Work alongside data scientists, ML engineers and game developers to integrate ML models into production systems, support deployment, conduct testing and troubleshoot performance or reliability issues in live environments Develop and maintain scripts, services or platform modules for feature pipelines, model orchestration, data-lake or lakehouse interactions (e.g., Spark, Redshift, Snowflake) Monitor, tune and optimise model performance, scalability, and cost-efficiency in production, contributing suggestions and implementation of improvements under the direction of senior engineers We are a company committed to creating diverse and inclusive environments where people can bring their full, authentic selves to work every day. We are an equal opportunity/affirmative action employer that believes everyone matters. Qualified candidates will receive consideration for employment regardless of their race, color, ethnicity, religion, sex (including pregnancy), sexual orientation, gender identity and expression, marital status, national origin, ancestry, genetic factors, age, disability, protected veteran status, military or uniformed service member status, or any other status or characteristic protected by applicable laws, regulations, and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application or recruiting process, please send a request to HR@insightglobal.com.To learn more about how we collect, keep, and process your private information, please review Insight Global's Workforce Privacy Policy: https://insightglobal.com/workforce-privacy-policy/. Skills and Requirements Master’s degree or equivalent in Computer Science, AI, ML, or related field, or Bachelor degree or equivalent in Computer Science, Electrical Engineering, or related field with 2+ years of software engineering experience with a focus on AI/ML systems or platform development Familiarity with deep-learning frameworks (e.g., PyTorch) and a basic understanding of machine learning lifecycle: model development, evaluation, deployment Proficiency in Python programming Experience working with containerization (Docker), orchestration (Kubernetes) and CI/CD pipelines in a cloud environment Experience with data-lake or lakehouse technologies (e.g., Spark, Redshift, Snowflake, Trino) Understanding of deploying and monitoring ML models in production, including performance, scalability, reliability, and cost considerations Strong problem-solving and collaboration skills, attention to detail, and excellent communication written and verbal Exposure to cloud platforms (AWS, GCP, or Azure) and infrastructure-as-code tooling (e.g., Terraform, CloudFormation) is highly desirable Exposure to generative AI technologies (e.g., diffusion models, large language models), experience in a live service or gaming environment, or prior project work in end-to-end ML systems Experience with one or more additional languages (e.g., Java, Go, C++)