Senior MLOps Engineer

TalentHue- Careers; Lahore; On-site; PKR 1,800,000 - 3,000,000; Full time

Job summary

TalentHue is seeking a Senior MLOps Engineer to operationalize ML models in production stacks leveraging GitLab CI/CD, Docker, Kubernetes, and Airflow. This is an onsite, full-time role in Lahore, Pakistan.

You will design and maintain retraining pipelines, implement model monitoring and drift detection, and collaborate with ML and DataOps teams to scale ML workloads from prototype to production.

Qualifications

• 3+ years in MLOps focusing on model lifecycle automation
• Kubernetes and Docker experience for serving ML workloads
• GitLab CI/CD pipelines for ML deployment and versioning
• Python skills for tooling and automation

Responsibilities

• Operationalizes ML models using GitLab CI/CD, Docker, Kubernetes, and Airflow for retraining pipelines.
• Implement production model monitoring, drift detection, and resource usage metrics.
• Maintain model registries and versioning across lifecycles.
• Collaborate with the AI/ML Engineer and AI Solution Architect to move from prototype to production.
• Coordinate with the Senior DataOps to align infra patterns across data and ML workloads.

Skills

Airflow

Docker

GitLab CI/CD

Kubernetes

ML Retraining

Python

Job description

A project-based software development company providing innovative digital solutions to businesses across multiple sectors. They focus on delivering reliable, scalable, and results-driven software products.

Senior MLOps Engineer

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
• Fulltime

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.

Expertise

Skills: Airflow, Docker, GitLab CI/CD, Kubernetes, ML Retraining

About TalentHue

TalentHue provides scalable, reliable Tech Recruitment, Corporate Recruitment and Consulting (Strategy, Operations, Performance) services. Our Recruitment and HR consultants will work alongside your team to meet the unique needs of your business.