Mlops Engineer (Remote)

We are seeking a highly skilled MLOps Engineer to join our team and play a critical role in designing, implementing, and maintaining scalable machine learning workflows. As an MLOps Engineer, you will collaborate with data scientists, software engineers, and DevOps teams to deploy, monitor, and optimize machine learning models in production. You will bring strong experience in machine learning pipelines, infrastructure automation, and a deep understanding of cloud-based architectures.

Key Responsibilities:

Model Deployment and Automation

• Design and implement end-to-end machine learning pipelines, from data ingestion to model deployment and monitoring.
• Automate the deployment of machine learning models using CI/CD workflows.
• Manage model versioning, testing, and rollback strategies.

Infrastructure Management

• Build and manage scalable cloud-based infrastructure for training, deployment, and inference (AWS, GCP, or Azure).
• Optimize compute resource allocation (GPU/CPU) for both training and serving environments.
• Containerize and orchestrate ML workloads using Docker and Kubernetes.

Collaboration and Development

• Partner with data scientists to understand model requirements and ensure seamless deployment.
• Collaborate with DevOps teams to integrate ML workflows into existing systems.
• Contribute to the development of internal tools for model tracking, monitoring, and retraining.

Monitoring and Optimization

• Monitor model performance and detect data/model drift, latency issues, or other anomalies.
• Implement solutions for continuous retraining and updating of models based on real-time data.
• Optimize model inference times and ensure system scalability.

Technical Skills:

• Proficiency in programming languages such as Python (preferred) or Java.
• Strong experience with machine learning frameworks (TensorFlow, PyTorch, Scikit-learn).
• Hands-on expertise with CI/CD tools (Jenkins, GitHub Actions, or similar).
• Deep knowledge of containerization (Docker) and orchestration (Kubernetes).
• Familiarity with model serving frameworks (e.g., TensorFlow Serving, FastAPI, TorchServe).
• Experience with MLOps platforms and tools like MLflow, Kubeflow, or SageMaker.
• Strong understanding of data engineering concepts, including ETL pipelines and feature stores.

Soft Skills:

• Ability to work in cross-functional teams and communicate effectively with data scientists, software engineers, and stakeholders.
• Strong problem-solving and troubleshooting skills.
• Eagerness to learn and adapt to new technologies.

Good to Have:

• Familiarity with cloud-based AI/ML platforms (AWS SageMaker, GCP Vertex AI, Azure ML).
• Experience with data orchestration tools like Apache Airflow or Prefect.
• Knowledge of distributed training frameworks (e.g., Horovod, Dask).
• Certifications in cloud platforms (e.g., AWS Certified Machine Learning Specialty).

Education and Experience:

• Bachelor’s or Master’s degree in Computer Science, Data Science, or a related field.
• 3 to 5 years+ years of experience in DevOps, MLOps, or related fields.
• Proven experience deploying ML models in production environments