Senior ML Platform Engineer

Reporting to the Director of Data and Machine Learning Platform, the Senior ML Platform Engineer within the Data Team will play a key role in researching and developing machine learning solutions to solve complex business problems. The Senior ML Platform Engineer will work closely with a cross-functional team to identify areas for improvement and design and implement scalable solutions. Relevant experience can range from working on a wide variety of optimization and classification problems, e.g. collaborative filtering/recommendation, fraud detection, segmentation, propensity modeling, text/sentiment classification, etc.

What you’ll do

• Design, build, and operate standardized training-to-serving 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, serverless inference where appropriate, blue/green and canary strategies, autoscaling policies, and cost controls (spot strategies, instance right-sizing).
• Implement ultra-low-latency serving patterns with Redis/Valkey: feature caching, online feature retrieval, request-scoped state, model response caching, and rate limiting/backpressure for bursty traffic.
• Provision and manage ML/data infrastructure with Terraform: SageMaker endpoints/configs, ECR/ECS/EKS resources, networking/VPC endpoints, ElastiCache/Valkey clusters, observability stacks, secrets, and IAM.
• Build platform abstractions and golden paths: Airflow DAG templates, CLI/SDKs, cookie-cutter repos, and CI/CD pipelines that take models from notebooks to production predictably.
• Establish and run 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 gates, model performance/latency SLOs, infra health dashboards, tracing, and alerting—plus incident response and postmortems.
• Partner with Security, SRE, and Data Engineering on private networking, policy-as-code, PII handling, least-privilege IAM, and cost-efficient architectures across environments.
• Evaluate, integrate, and rationalize platform tooling (e.g., MLflow registry, feature stores, serving gateways); lead migrations with clear change management and minimal downtime.

What you’ll bring

• 5+ years building and operating production-grade ML/data platforms with a focus on serving, reliability, and developer experience.
• Strong software engineering in Python, Go, or Java; experience building resilient services, APIs, and automation tooling with high test coverage.
• Deep experience with AWS SageMaker inference: endpoint configuration, containerization, model packaging, autoscaling, serverless vs. real-time trade-offs, MME, A/B and canary releases.
• Expertise with online feature stores like Redis/Valkey in ML serving contexts.
• Proven Terraform experience managing ML and data infra end-to-end: modules, workspaces, drift detection, change reviews, and safe rollbacks; familiarity with GitOps patterns.
• Airflow orchestration at scale: 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 Worflows: metrics/logs/traces, performance profiling, capacity planning, cost monitoring, and runbooks.
• Excellent communication and cross-functional collaboration with Data Science, Data Engineering, DevOps and Backend.

Why Mistplay?

We strive to make our work environment as inviting and fun as possible! Working at is coupled with a whole array of perks that we've adopted virtually and in-person: Team Lunches, game nights, company-wide events, and so much more. Our culture is deeply rooted in growth and upheld by a team of smart, dynamic, and enthusiastic people. We utilize data to constantly learn, improve, and adapt. We foster an environment where everyone is encouraged to share their ideas, push boundaries, take calculated risks, and witness their visions come to life.