MLOps Engineer

We are looking for a highly skilled MLOps Engineer to join our AI and engineering team. In this role, you will be responsible for building, deploying, monitoring, and maintaining scalable machine learning infrastructure and production-ready AI systems. You will work closely with data scientists, AI/ML engineers, software developers, and DevOps teams to streamline the machine learning lifecycle, automate model deployment, and ensure reliable, secure, and high-performing AI applications.

Key Responsibilities
Design, build, and maintain scalable MLOps pipelines for machine learning model training, deployment, and monitoring.
Automate the end-to-end machine learning lifecycle, including data validation, model training, testing, deployment, and versioning.
Deploy machine learning models to production using containerization and orchestration technologies.
Monitor model performance, detect model drift, and implement retraining strategies.
Build and maintain CI/CD pipelines for machine learning workflows.
Manage model versioning, experiment tracking, and artifact repositories.
Collaborate with AI/ML engineers and data scientists to productionize machine learning models.
Optimize infrastructure for scalability, reliability, cost efficiency, and high availability.
Implement security, governance, and compliance best practices for AI systems.
Develop monitoring, logging, and alerting solutions for machine learning services.
Troubleshoot and resolve production issues related to ML infrastructure and deployments.
Stay updated with emerging MLOps tools, frameworks, and cloud technologies.
Required Qualifications
Bachelor's degree in Computer Science, Software Engineering, Artificial Intelligence, Data Science, or a related field (or equivalent practical experience).
Proven experience as an MLOps Engineer, DevOps Engineer, or Machine Learning Infrastructure Engineer.
Strong programming skills in Python and experience with scripting languages such as Bash.
Experience with machine learning frameworks such as TensorFlow, PyTorch, or Scikit-learn.
Strong understanding of CI/CD pipelines and DevOps practices.
Experience with containerization technologies such as Docker and Kubernetes.
Familiarity with cloud platforms including AWS, Microsoft Azure, or Google Cloud Platform.
Experience with infrastructure as code (Terraform, CloudFormation, or similar).
Knowledge of model monitoring, model versioning, and experiment tracking tools.
Strong analytical, troubleshooting, and communication skills.
Preferred Qualifications
Experience with MLflow, Kubeflow, Airflow, or Vertex AI Pipelines.
Knowledge of feature stores, data versioning, and model registries.
Experience with distributed computing frameworks such as Apache Spark.
Familiarity with monitoring tools such as Prometheus, Grafana, or ELK Stack.
Understanding of Responsible AI, model governance, and security best practices.
Experience working in Agile or Scrum development environments.
Technical Skills
Programming Languages: Python, Bash, SQL
Machine Learning Frameworks: TensorFlow, PyTorch, Scikit-learn
MLOps Tools: MLflow, Kubeflow, Airflow, DVC, Weights & Biases
Containerization & Orchestration: Docker, Kubernetes
Cloud Platforms: AWS SageMaker, Microsoft Azure ML, Google Vertex AI
CI/CD & DevOps: Jenkins, GitHub Actions, GitLab CI/CD, Azure DevOps
Infrastructure as Code: Terraform, CloudFormation
Monitoring & Logging: Prometheus, Grafana, ELK Stack
Version Control: Git, GitHub, GitLab
What We Offer
Competitive salary and comprehensive benefits package.
Flexible work environment (onsite, hybrid, or remote).
Opportunities to work on large-scale AI and machine learning production systems.
Access to cutting-edge cloud, MLOps, and AI technologies.
Continuous learning through training, certifications, and industry conferences.
Collaborative, innovative, and inclusive engineering culture.
Career growth opportunities in AI infrastructure, cloud engineering, and machine learning operations.