Build and manage scalable machine learning infrastructure and deployment pipelines. Responsibilities include automating training, deployment, monitoring, and retraining workflows; deploying ML solutions on cloud platforms; implementing model governance, versioning, and performance tracking; managing cloud-based ML infrastructure; and collaborating with data science, engineering, architecture, and business teams to productionize models.
This is a remote position.
ShuruTech is a rapidly growing B2B IT services company specializing in building scalable, secure, and tailored software solutions for global clients. Our focus spans across CRM development, product engineering, cloud transformation, and automation. We’re passionate about empowering businesses to innovate and grow with custom technology solutions. Shuru is looking for an experienced MLOps Engineer to build and manage scalable machine learning infrastructure and deployment pipelines. You will work closely with Data Scientists, ML Engineers, Architects, and business stakeholders to take ML models from development to production.
Key Responsibilities
- Build and automate ML pipelines for training, deployment, monitoring, and retraining.
- Deploy and manage machine learning solutions on cloud platforms, preferably Azure.
- Implement model monitoring, governance, versioning, and performance tracking.
- Collaborate with Data Science and Engineering teams to productionize ML models.
- Manage cloud-based ML services and infrastructure.
- Improve MLOps platforms, tools, and deployment practices.
- Work with stakeholders and technology partners to deliver scalable ML solutions.
Requirements
- 5-8 years of experience in ML Engineering, MLOps, or related roles.
- Strong hands-on experience with Python.
- Experience with TensorFlow, PyTorch, or Scikit-learn.
- Strong understanding of the ML lifecycle and model deployment.
- Experience with cloud platforms such as Azure, AWS, or GCP.
- Experience building automated CI/CD and ML pipelines.
- Knowledge of Docker, Kubernetes, MLflow, or Kubeflow.
- Strong communication and stakeholder management skills.
Benefits
- Competitive compensation and benefits.
- Remote and flexible work environment.
- Opportunity to shape technology strategy and business outcomes.
- Strong learning and leadership growth opportunities.
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