Senior MLOps Engineer

TalentHue - IT & Corporate Recruitment; Lahore; On-site; PKR 1,116,000 - 1,674,000; Full time

Job summary

TalentHue - IT & Corporate Recruitment is looking for a DevOps professional with strong MLOps expertise. The ideal candidate should have 5 - 6 years of experience in DevOps/Platform Engineering, with a focus on machine learning operations.

This role requires deep knowledge of GitLab CI/CD, Kubernetes, and Docker, particularly for ML workloads. Candidates will design and monitor retraining pipelines and implement model performance metrics to ensure efficient operations.

Qualifications

• 5 - 6 years in DevOps/Platform Engineering, with 3+ years focused on MLOps.
• Strong hands-on experience in Kubernetes and Docker for ML workloads.
• Proven experience building GitLab CI/CD pipelines for ML model deployment.

Responsibilities

• Operationalize ML models within the stack using GitLab CI/CD.
• Containerize ML models and manage deployment via Kubernetes.
• Design and operate Airflow-based retraining pipelines.

Skills

GitLab CI/CD

Kubernetes

Docker

Airflow

Python

Job description

Role Introduction

Operationalizes ML models within the stack using GitLab CI/CD, Docker, Kubernetes, and Airflow for retraining pipelines, alongside production model monitoring and drift detection.

Features

• Onsite

Requirements

• Build and maintain CI/CD pipelines specifically for ML model deployment, versioning, and rollback (GitLab CI).
• Containerize ML models and manage deployment via Kubernetes, ensuring scalable and reliable serving infrastructure.
• Design and operate Airflow-based retraining pipelines, scheduling and monitoring model refresh cycles.
• Implement production model monitoring, performance metrics, latency, and resource usage.
• Build drift detection mechanisms to flag when models need retraining or investigation.
• Maintain model registries and versioning to ensure traceability across model lifecycle stages.
• Partner with the AI/ML Engineer and AI Solution Architect to standardize the path from prototype to production.
• Coordinate with the Senior DataOps role to align infrastructure patterns across data and ML workloads where they overlap.

Specifications

• 5 - 6 years in DevOps/Platform Engineering, with 3+ years specifically focused on MLOps (model lifecycle automation, not general infra work).
• Strong hands-on Kubernetes and Docker experience for serving and scaling ML workloads specifically.
• Proven experience building GitLab CI/CD pipelines for ML model deployment and versioning.
• Experience operating Airflow for retraining pipeline orchestration (distinct from general data pipeline orchestration).
• Practical experience implementing model monitoring and drift detection in a production environment.
• Solid Python skills for tooling, automation, and integration with ML frameworks.