Senior MLOps Engineer – Digital Transformation
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Job Description
MLOps Engineer optimizing scalable machine learning infrastructure at a leading tech firm specializing in digital transformation and IT consulting.
Responsibilities:
- Design and maintain robust ML deployment pipelines to ensure seamless model delivery.
- Automate model training, deployment, and monitoring workflows to increase operational efficiency.
- Collaborate closely with Data Scientists and Engineering teams to integrate models into production environments.
- Optimize cloud-based infrastructure to enhance the scalability and reliability of ML systems.
- Implement CI/CD best practices specifically tailored for machine learning lifecycles.
- Monitor production systems and proactively troubleshoot performance or governance issues.
Requirements:
- Extensive experience as an MLOps Engineer or Machine Learning Engineer within production environments.
- Advanced proficiency in Python.
- Proven track record deploying, monitoring, and maintaining ML models at scale.
- Hands-on experience with Docker and Kubernetes for containerization and orchestration.
- Strong expertise in cloud platforms such as AWS, Azure, or GCP.
- Deep understanding of model governance and the end-to-end ML lifecycle.
- Experience with CI/CD pipelines for machine learning workflows is required.
- Proficiency with ML orchestration tools such as Kubeflow, Airflow, or MLFlow is highly preferred.
- Experience with Infrastructure as Code (IaC) using Terraform is preferred.
- Familiarity with monitoring and observability tools in a distributed environment is a plus.
- Experience with LLMOps or GenAI pipelines is a significant plus.
- Background working with enterprise-scale infrastructure or within regulated, high-compliance environments is a plus.
- Familiarity with managed platforms like Databricks, SageMaker, or Vertex AI is preferred.
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