~26d ago

MLOps / ML Platform Engineer

(united States)Remotemid

OtherMl Platform Engineer

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Quick Summary

Key Responsibilities

Manage data, training, serving, and inference systems for high-throughput model workflows. Build scalable pipelines: Implement reproducible training and evaluation pipelines with versioning,

Technical Tools

OtherMl Platform Engineer

SumerSports is a leading football intelligence technology company that specializes in providing an innovative suite of products for football fans and NFL clubs. We are a collection of executives, engineers, data scientists, and visionaries from NFL clubs, technology startups, finance, and academia.

Responsibilities

~1 min read

→Design and operate ML infrastructure: Manage data, training, serving, and inference systems for high-throughput model workflows.
→Build scalable pipelines: Implement reproducible training and evaluation pipelines with versioning, scheduling, and artifact tracking.
→Optimize compute and cost: Tune GPU and CPU workloads, manage clusters, and drive efficiency via rightsizing, spot scheduling, and caching.
→Serve models in production: Operate APIs for low-latency inference with autoscaling, blue-green or canary rollouts, and rollback safety.
→Ensure reliability and observability: Define and own SLOs; instrument pipelines and services to track latency, cost, drift, and data quality.
→Secure and automate: Manage IAM, secrets, and container security; automate deployment pipelines via CI/CD and infrastructure as code.
→Collaborate cross-functionally: Partner with research scientists and AI engineers to deliver models from experiment to production with minimal friction.
→Document and enable: Build templates, runbooks, and internal tooling that make ML workflows repeatable, safe, and fast.

Requirements

~1 min read

4+ years of experience in ML platform, DevOps, or infrastructure engineering.
Deep knowledge of Kubernetes, CI/CD, containers, and cloud infrastructure (AWS, GCP, or Azure).
Hands-on experience managing GPU clusters and training/inference pipelines.
Familiarity with data orchestration and storage formats (Delta, Parquet, Polars, Spark).
Proven ability to ship and operate production ML systems with SLOs.
Strong Python skills and comfort with infrastructure as code and automation.
Experience with observability and cost optimization at scale.