MLOps Engineer

General Information

Req #: WD

Career area:
Information Technology

Country/Region:
United States of America

State:
North Carolina

City:
Morrisville

Date:
Tuesday, February 17, 2026

Working time:
Full-time

Additional Locations:
United States of America - North Carolina - Morrisville

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Lenovo is a US $69 billion revenue global technology powerhouse, ranked #196 in the Fortune Global 500, and serving millions of customers every day in 180 markets. Focused on a bold vision to deliver Smarter Technology for All, Lenovo has built on its success as the world's largest PC company with a full‑stack portfolio of AI‑enabled, AI‑ready, and AI‑optimized devices (PCs, workstations, smartphones, tablets), infrastructure (server, storage, edge, high performance computing and software‑defined infrastructure), software, solutions, and services.

Lenovo's continued investment in world‑changing innovation is building a more equitable, trustworthy, and smarter future for everyone, everywhere. Lenovo is listed on the Hong Kong stock exchange under Lenovo Group Limited (HKSE: 992) (ADR: LNVGY).

This transformation together with Lenovo's world‑changing innovation is building a more inclusive, trustworthy, and smarter future for everyone, everywhere. To find out more visit , and read about the latest news via our Story Hub.

As a MLOps Engineer, you will design, build, and operate the MLOps Control Plane and supporting systems that enable automated, production‑grade ML workflows tightly integrated with our GPU‑centric infrastructure. You will own end‑to‑end pipelines for model registry, adapter standardization, automated distillation/retraining, data lineage, drift detection/triggers, multi‑adapter model serving, and dynamic routing‑while ensuring deep awareness of underlying hardware (dynamic GPU partitioning, time‑slicing, resource observability, and high‑speed interconnects).

This role demands strong software engineering, Dev Ops discipline, and a passion for scaling AI systems reliably at cluster scale.

• Architect and implement the MLOps Control Plane
  , including model registry, versioning, promotion, and governance features.
• Develop and maintain the Data Adapter SDK for standardized data ingestion across diverse sources, ensuring 100% adoption and versioning.
• Build automated CI/CD pipelines for model distillation, retraining (triggered by drift/concept shift), and multi‑adapter deployment.
• Implement data lineage tracking
  , automated drift detection, and real‑time triggers for retraining or routing changes.
• Design multi‑adapter serving infrastructure with dynamic model routing, supporting heterogeneous models and hardware‑aware inference.
• Integrate MLOps workflows with GPU infrastructure features: in‑place container resizing, GPU memory observability, dynamic partitioning/time‑slicing, failure analysis, and high‑performance networking (RoCE/Infini Band tuning awareness).
• Own production observability, alerting, and automated root‑cause analysis for ML pipelines and GPU workloads to meet 98% success rate targets.
• Collaborate across pillars to ensure MLOps systems leverage hardware/software infra improvements for efficiency gains (e.g., 5%+ reduction in training step time).
• Drive agility goals: enable