About the Role
As an MLOps Engineer, you will bridge the gap between data science and operations.
The role focuses on building robust, scalable infrastructure for training, deploying, and monitoring ML models.
You will work closely with data scientists and DevOps teams to streamline the ML lifecycle.
Key Responsibilities
- Design and implement ML training and inference pipelines
- Build and maintain model versioning and experiment tracking systems
- Deploy models to production with monitoring and alerting
- Optimize model performance and resource utilization
- Automate ML workflows and CI/CD for model deployment
First 90 Days
- Understand current ML infrastructure and identify improvement areas
- Implement model monitoring and alerting for production models
- Establish MLOps best practices and documentation
Expected Outcome: After 90 days, you will be managing the ML infrastructure and driving improvements in model deployment efficiency.
Challenges
- Managing diverse ML workloads with varying resource requirements
- Ensuring model reliability and performance in production
- Keeping up with rapidly evolving MLOps tools and practices
Requirements
- 3+ years of experience in DevOps, infrastructure, or ML engineering
- Strong knowledge of Docker, Kubernetes, and cloud platforms
- Experience with ML frameworks and model serving (MLflow, Kubeflow, TensorFlow Serving)
- Programming skills in Python and infrastructure-as-code tools
- Understanding of ML model lifecycle and monitoring
This Role Does Not Include
- Building ML models from scratch (support role for data scientists)
- End-user support
- Hardware maintenance
What We Offer
- Work with cutting-edge MLOps technologies
- Cloud and ML certification budget
- Flexible remote work arrangements
- Collaborative engineering culture
About Us
We develop technology solutions with a strong focus on quality, collaboration, and sustainable growth. Our work culture is built on professionalism, trust, and openness at all levels.