Job Specifications
JOB DESCRIPTION
Insight Global is looking for a Senior 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 Senior Software Engineer with expertise in AI/ML systems and platform development, you will help lead the creation of a scalable AI Platform. You will report to the Engineering Director of the AI Platform team. Key responsibilities include but are not limited to: • Lead the architecture, design, and development of next-generation AI platforms supporting the entire AI lifecycle (data ingestion, feature store, model training, validation, deployment, monitoring, feedback loops) in a live-service gaming environment
• Define and architect scalable, secure, high-performance multi-cloud platforms to support global real-time analytics, model inference at scale, low latency, and high availability for game services and live ops
• Own the integration of AI/ML solutions into the real-time production game environment: working with producers, game engineers, ML engineers, data scientists and live-ops teams to ensure seamless model deployment, performance monitoring, rollback strategies, drift detection, and lifecycle management
• Drive automation of end-to-end workflows: CI/CD for ML (MLOps), model versioning, A/B testing, feature-pipeline orchestration, self-service tooling for internal users (game devs, data scientists) to rapidly adopt the platform
• Establish and enforce platform-wide standards for reliability, scalability, cost-optimization, operational excellence, security, and governance AI practices (in line with industry trends)
• Mentor and lead other junior engineers on the team: coaching architecture decisions, best practices for ML platforms, cloud infrastructure, observability, and operational excellence in live systems
• Partner with game-studio producers, artists, game engineers, data science and analytics teams and live-ops to define, develop and deliver customer-facing AI solutions: translate studio business/game challenges into AI-platform deliverables, propose reusable solutions for common partner use-cases (e.g., personalization recommendation, generative content, player-engagement, anti-cheat), and lead proof-of-concepts or pilots.
• Act as a strategic bridge between business/game-service priorities and platform delivery: translate game-service and player-experience requirements into platform features, articulate trade-offs (cost, latency, risk, security), drive cross-functional alignment, ensure platform empowers business outcomes and partner success
REQUIRED SKILLS AND EXPERIENCE
• Master’s degree (or equivalent) in Computer Science, AI, ML, or related field with 5+ years of professional software engineering experience focused on AI/ML systems, platform development, data/analytics infrastructure, and/or live-service game systems
• Excellent programming skills in Python, and strong familiarity with one or more other languages (e.g., Java, C++, Go) as required by high-performance infrastructure
• Deep experience with deep-learning frameworks such as PyTorch, and exposure to generative AI technologies (LLMs, diffusion models, agentic systems) used in production settings
• Proven track record building and operating cloud-based, scalable, secure, production-grade platforms supporting large-scale ML workloads
• Expertise with major cloud platforms (AWS, GCP or Azure), and proficiency in cloud-native tooling: infrastructure-as-code (Terraform, CloudFormation), containerization (Docker), orchestration (Kubernetes), CI/CD pipelines, monitoring/logging/observability
• Strong experience deploying and managing ML models in production, especially in real-time or near-real-time use-cases (e.g., live games, personalization engines, recommendation systems, player-engagement features)
• Demonstrated ability to architect and deliver platform features that enable self-service, internal developer productivity, governance and compliance, including model monitoring, drift detection, observability, and performance management across global scale
• Excellent leadership, mentorship and communication skills: