Machine Learning Engineer-ML Ops

About You (Skills & Experience Required)

Bachelor’s or master’s degree in computer science, Engineering, or a related field. 

5+ years of experience in machine learning, data engineering, or software development. 

Good experience in building data pipelines, data cleaning, and feature engineering is essential for preparing data for model training.

Knowledge of programming languages (Python, R), and version control systems (Git) is necessary for building and maintaining MLOps pipelines.

Experience with MLOps-specific tools and platforms (e.g., Kubeflow, MLflow, Airflow) can streamline MLOps workflows.

DevOps principles, including CI/CD pipelines, infrastructure as code (IaaC), and monitoring is helpful for automating ML workflows.

Experience with atleast one of the cloud platforms (AWS, GCP, Azure) and their associated services (e.g., compute, storage, ML platforms) is essential for deploying and scaling ML models.

Familiarity with container orchestration tools like Kubernetes can help manage and scale ML workloads efficiently.

It would be great if you also had,

Experience with big data technologies (Hadoop, Spark).

Knowledge of data governance and security practices.

Familiarity with DevOps practices and tools.

What will you be doing in this role? 

     Model Deployment & Monitoring: 

Oversee the deployment of machine learning models into production environments. 

Ensure continuous monitoring and performance tuning of deployed models. 

Implement robust CI/CD pipelines for model updates and rollbacks. 

Collaborate with cross-functional teams to understand business requirements and translate them into technical solutions. 

Communicate project status, risks, and opportunities to stakeholders.

Provide technical guidance and support to team members. 

Infrastructure & Automation: 

Design and manage scalable infrastructure for model training and deployment.

Automate repetitive tasks to improve efficiency and reduce errors. 

Ensure the infrastructure meets security and compliance standards. 

Innovation & Improvement: 

Stay updated with the latest trends and technologies in MLOps.

Identify opportunities for process improvements and implement them. 

Drive innovation within the team to enhance the MLOps capabilities.