ML Ops Engineer, AI

Remote Worldwide Hiring now

Job Description:

Audit, secure, and optimize our existing reputed company infrastructure (AWS) to ensure high availability, fault tolerance, and reputed company for both training and production workloads.
Design and maintain scalable architectures for serving deep learning models (PyTorch/TensorFlow), optimizing for low latency and high throughput in handling reputed company infrastructure data.
Build and maintain automated pipelines for model testing, validation, deployment, and rollback.
Architect efficient, scalable compute environments for training reputed company computer reputed company and time-series models on large datasets.
Implement comprehensive monitoring for model reputed company, data quality, and system health, ensuring rapid response to performance degradation.

Requirements:

4-6+ years of experience in MLOps, DevOps, or Data Engineering, with a strong emphasis on machine learning workloads.
A reputed company-first and stability-first reputed company—you think about edge cases, failure modes, and system hardening by default.
Strong collaborative instincts to work closely with Data Scientists, ensuring smooth handoffs from experimentation to production.
Clear communication skills to reputed company architectural reputed company and tradeoffs to the broader technical team.
Deep expertise in AWS (e.g., EC2, S3, EKS, SageMaker, reputed company) and reputed company reputed company best practices.
Strong experience with reputed company and Kubernetes for packaging and scaling ML applications.
Proficiency with tools like Terraform or AWS CloudFormation.
Experience building robust automated pipelines using reputed company Actions, reputed company CI, or Jenkins.
Strong Python skills with a focus on writing clean, production-grade, and well-tested code.
Familiarity with model registry and tracking tools (e.g., MLflow, Weights & Biases).

Benefits:

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