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Core & ML Ops Team Lead - Remote

1 month ago
Hungary, Poland, Portugal, Spain, Brazil
Full-time
Lead
Machine Learning Engineer
DevOps and Infrastructure
Apache Airflow C++ CI/CD Docker Go Java Kafka Kubernetes Linux Mesos Python Rust Secrets Management TCP/IP
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Zyte

Zyte

Zyte is a leading provider of Full Stack Web Scraping API and World Class Data Extraction Services. With AI-powered web scraping platform, Zyte offers fast and reliable data extraction solutions for over 2,000 companies and 1 million developers worldwi...

Professional Services
251-1K
Founded 2010
$3M raised
View All Jobs 6

Description

  • Lead and manage the Core & MLOps squad, including roadmap, prioritization, delivery, mentoring, and enforcement of high engineering standards.
  • Design, evolve, and own the core platform infrastructure (container orchestration, GPU scheduling/autoscaling, and distributed compute) that powers Zyte at scale.
  • Own and operate the model platform including model registry, experiment tracking, training orchestration, evaluation framework, serving infrastructure, and model monitoring.
  • Build and maintain the Golden Path: reference repositories, scaffold CLI, opinionated CI/CD pipelines, runtime contracts (health/metrics/tracing/SLOs), templates, and production-ready defaults.
  • Provide turnkey serving patterns (online and batch), drift/quality monitoring, rollback playbooks, and integrate managed AI capabilities with cost and data-governance guardrails.
  • Develop and maintain operators, sidecars, internal SDKs/libraries, and high-performance clients that enforce platform contracts and reliability defaults.
  • Partner with product engineering, Prod Ops, and Security to drive platform adoption, rollout plans, and cross-team integrations.
  • Run observability and billing pipelines (logging/metrics/tracing; metering/events/cost tracking) and lead efforts in supply-chain security (SBOM, image signing).
  • Champion SRE practices including SLIs/SLOs, incident management, reliability enablement, and cost governance across the platform.

Requirements

  • 5+ years of experience building distributed systems.
  • 3+ years of experience in MLOps/ML platform engineering or equivalent impact.
  • Strong knowledge of Linux/OS internals (process model, cgroups/namespaces), networking (TCP/IP, HTTP/2), concurrency, and performance profiling.
  • Deep understanding of Kubernetes (knowledge of Mesos is a bonus).
  • Proficiency developing high-performance services in Java, Rust, Go, or C++ with strong Python skills (experience with vert.x and Netty is a bonus).
  • Experience with GPU infrastructure including scheduling, containerization, and optimization.
  • Proven track record designing and operating model platforms in production (registry, training, serving, monitoring).
  • Demonstrated success leading technical teams and implementing organization-wide platform solutions.
  • Preferred: experience with streaming and workflow tools (Kafka, Argo, Temporal, Airflow), eBPF-based observability or perf tooling (io_uring), cost optimization for ML/AI, multi-tenant quotas/fairness, hands-on Golden Path authoring, and SRE practices (SLIs/SLOs, incident management).

Benefits

  • Fully remote work with freedom and flexibility to work from where you do your best work.
  • Be part of a self-motivated, progressive, multi-cultural, globally distributed team.
  • Opportunity to work with cutting-edge open-source technologies and tools.
  • Supportive environment that fosters new ideas and bringing them to market.

Interested in this position?

Apply directly on the company website

Apply Now

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