Hybrid Senior ML Platform Engineer II

at Mistplay

• Job title: Senior ML Platform Engineer II
• Job type: Full Time
• Experience level: Senior
• Salary: Not specified
• Degree requirement: Bachelor's Degree
• Tech skills: Airflow, AWS, Flux, Java, Python, PyTorch, Redis, Scikit-Learn, Tensorflow, Terraform, TypeScript, Vue.js, Go
• Location requirements: Hybrid, Toronto, Canada

About the role

• Senior ML Platform Engineer at Mistplay researching and developing machine learning solutions. Collaborating with teams to solve complex business problems and enhance mobile gaming experience.

Responsibilities

• Design, build, and operate standardized training-to-deployment pipelines with Airflow, covering artifact management, environment provisioning, packaging, deployment, and rollback for SageMaker endpoints.
• Own real-time and batch inference on SageMaker: multi-model endpoints (MME), serverless inference where appropriate, blue/green and canary deployment strategies, autoscaling policies, and cost controls (spot strategies, instance sizing).
• Implement very low-latency service models using Redis/Valkey: feature caching, online feature retrieval, request-level state, model response caching, and rate limiting/backpressure for bursty traffic.
• Provision and manage ML/data infrastructure with Terraform: SageMaker endpoints/configurations, ECR/ECS/EKS resources, network endpoints/VPCs, ElastiCache/Valkey clusters, observability stacks, secrets, and IAM.
• Build platform abstractions and golden paths: Airflow DAG templates, CLI/SDK, cookie-cutter repositories, and CI/CD pipelines that move models from notebooks to production predictably.
• Establish and manage model lifecycle governance: model/feature registries, approval workflows, promotion policies, lineage and audit trails integrated with Airflow runs and Terraform state.
• Implement end-to-end observability: data/feature freshness checks, drift/quality controls, model performance/latency SLOs, infrastructure health dashboards, tracing and alerts, plus incident response and postmortems.
• Collaborate with security, SRE, and data engineering teams on private networks, policy-as-code, handling of PII, least-privilege IAM, and cost-effective architectures across environments.
• Evaluate, integrate, and rationalize platform tooling (e.g., MLflow registry, feature stores, service gateways); lead migrations with clear change management and minimal downtime.

Requirements

• 5+ years of experience building and operating production-grade ML/data platforms focused on service, reliability, and developer experience.
• Strong software engineering skills in Python, Go, or Java; experience building resilient services, APIs, and automation tools with high test coverage.
• Deep experience with AWS SageMaker inference: endpoint configuration, containerization, model packaging, autoscaling, trade-offs between serverless and real-time, MME, A/B and canary releases.
• Expertise with online feature stores such as Redis/Valkey in ML service contexts.
• Proven Terraform experience for end-to-end ML and data infrastructure management: modules, workspaces, drift detection, change review, and safe rollbacks; familiarity with GitOps patterns.
• Large-scale Airflow orchestration: dependency modeling, sensors, retries, SLAs, backfills, DAG factories, and integrations with registries, artifact stores, and Terraform pipelines.
• Familiarity with ML frameworks (scikit-learn, XGBoost, PyTorch, TensorFlow) from a platform integration perspective to support diverse runtimes and containers.
• Observability for ML workflows: metrics/logs/traces, performance profiling, capacity planning, cost monitoring, and runbooks.
• Excellent cross-functional communication and collaboration with data science, data engineering, DevOps, and backend teams.

Benefits

• Team lunches
• Game nights
• Company-wide events