Develop and maintain CI/CD pipelines for deploying ML and LLM models in production, ensuring rapid and reliable model iteration.
Optimize the deployment process for LLMs, considering cost, latency, and scalability constraints.
Automate model retraining and redeployment processes to respond to changing data patterns.
Set up and manage cloud and on-premise infrastructure for ML/LLM workloads on platforms such as AWS, GCP, or Azure.
Work with containerization and orchestration tools (e.g., Docker, Kubernetes) to ensure scalable and resilient model deployment.
Implement data versioning and validation processes to maintain data integrity throughout the ML lifecycle.
Use tools like MLflow for tracking experiments, managing model versions, and maintaining reproducibility.
Document MLOps/LLMOps practices and mentor team members on deployment best practices.

Experience in MLOps, DevOps, or a related role, with hands-on experience deploying ML/LLM models.
Proficiency in cloud platforms (AWS, Azure, GCP) and containerization (Docker, Kubernetes).
Experience with CI/CD tools (e.g. Github Actions or Jenkins) and ML workflow tools (e.g., MLflow, Kubeflow).
Strong scripting skills (Python, Bash) and familiarity with infrastructure-as-code tools (e.g., Terraform) and experience with Git or version control mechanisms.
Strong analytical and problem-solving skills.

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Senior ML Ops Engineer

Key skills