Core & ML Ops Team Lead

Jobgether

10h•Remote

About The Position

This role is ideal for an experienced technical leader in MLOps and distributed systems, responsible for building and maintaining the scalable infrastructure that supports mission-critical services. You will lead a cross-functional team in designing platforms for model training, orchestration, deployment, and monitoring while ensuring high performance, reliability, and security. The position combines hands-on engineering with strategic team leadership, driving adoption of best practices, automation, and observability across the organization. You will collaborate with product, operations, and security teams to implement robust platforms that empower engineers to build and deploy services confidently. Mentorship, knowledge sharing, and establishing production-ready standards are central to your impact. This role allows you to shape platform strategy while staying deeply engaged in cutting-edge technologies and ML operations at scale.

Requirements

5+ years building distributed systems; 3+ years in MLOps or ML platform engineering
Strong knowledge of Linux/OS internals, networking, concurrency, and performance profiling
Deep expertise in Kubernetes (bonus: Mesos) and GPU infrastructure management
Proficiency in high-performance programming (Java, Rust, Go, C++; strong Python skills)
Experience designing and operating production model platforms (registry, training, serving, monitoring)
Proven experience leading technical teams and implementing organization-wide platform solutions
Familiarity with CI/CD, SRE practices, observability, and reliability enablement
Strong collaboration, mentoring, and communication skills

Nice To Haves

Experience with streaming/workflow tools (Kafka, Argo, Temporal, Airflow)
Hands-on work with eBPF observability, perf tooling, or io_uring
Expertise in ML/AI cost optimization, multi-tenant quotas, and fairness
Experience authoring Golden Paths (service templates, CI/CD blueprints, scaffolds)

Responsibilities

Lead the Core & MLOps team, overseeing roadmap, prioritization, delivery, and mentoring
Design, develop, and maintain scalable infrastructure for model training, serving, and monitoring
Build and maintain the Golden Path: reference repositories, scaffold CLIs, CI/CD pipelines, runtime contracts, and production-ready defaults
Operate secure, multi-tenant model registries and orchestration platforms with standardized experiment and evaluation frameworks
Integrate AI/ML capabilities as managed platform services with cost and governance controls
Collaborate with product engineering, operations, and security teams on adoption and rollout plans
Promote best practices in observability, reliability, cost governance, and platform standardization