DevOps + MLOps Engineer - CodeCraft Technologies

We are looking for a self-driven DevOps + MLOps Engineer with expertise in building and maintaining scalable infrastructure, CI/CD pipelines, and ML model deployment workflows. The role requires strong collaboration with developers, QA, data scientists, and ML engineers.

Key Responsibilities

CI/CD & DevOps

Implement and maintain robust CI/CD pipelines for applications and ML models (Jenkins, GitHub Actions, GitLab CI, Azure DevOps).

Automate testing and validation processes for software and ML workflows.

Collaborate with development and data science teams to embed DevOps practices into the lifecycle.

MLOps & ML Workflow Automation

Design and manage ML pipelines using MLflow, Kubeflow, or Airflow.

Implement model versioning, tracking, reproducibility, and automated retraining pipelines.

Deploy ML models into production in containerized environments (Docker, Kubernetes).

Monitor ML models for performance, accuracy, and data drift using tools like Evidently AI, Prometheus, or Grafana.

Infrastructure as Code & Cloud

Define and manage infrastructure using Terraform (or equivalent) for AWS, GCP, or Azure.

Optimize cloud infrastructure for scalability, resilience, and cost efficiency.

Ensure infrastructure security and compliance with ISO 9001 (QMS) and ISO 27001 (ISMS).

Logging, Monitoring & Security

Set up logging and monitoring systems (ELK Stack, Grafana, or Cloud-native tools).

Conduct vulnerability assessments, security audits, and incident response.

Implement security best practices and collaborate with security teams.

Collaboration & Documentation

Work with QA, third-party hardware providers, and cross-functional teams for seamless integration.

Maintain systematic documentation of infrastructure, pipelines, and compliance records.

Required Skills

Strong expertise with CI/CD tools (Jenkins, GitHub Actions, GitLab CI, Azure DevOps).

Hands-on with Kubernetes, Docker, Terraform, and Python.

Experience in AWS, GCP, and/or Azure cloud environments.

Familiarity with ML pipeline tools (MLflow, Kubeflow, Airflow).

Experience deploying and monitoring ML models in production.

Knowledge of logging/monitoring tools (ELK, Prometheus, Grafana).

Solid understanding of security practices and compliance standards.

Preferred Qualifications

Exposure to ML model monitoring & data drift detection.

Prior experience collaborating with ML or Data Science teams.

Familiarity with GCP Security Command Center, AWS Security Hub.

Knowledge of ISO 9001 & ISO 27001 documentation practices.