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Remote, popular ML title with mid-level expectations and broad full-stack/MLOps requirements increases competition.
Deep MLOps, LLM, and backend expertise required makes cross-industry transfers limited.
Multiple mandatory MLOps, CI/CD, cloud, and rigorous testing requirements create strict technical filters.
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Architect and maintain high-performance, fault-tolerant backend systems using Python and FastAPI.
Build and optimize scalable MLOps pipelines and infrastructure with AWS, Docker, Kubernetes, and CI/CD automation tools.
Design and maintain comprehensive automated testing frameworks for software and ML model validation.
Proficient in Python and FastAPI backend development.
Experience building and maintaining MLOps pipelines with AWS, Docker, Kubernetes, and CI/CD tools like Jenkins or GitHub Actions.
Bachelor's or Master's degree in Computer Science, Engineering, or related technical field.
Work Experience Required: Not explicitly mentioned in the JD.
Strong expertise in MLOps engineering and infrastructure reliability with operational mindset.
Experience in designing scalable data systems (SQL and NoSQL) under heavy data loads.
Familiarity with AI/LLM technologies and deployment in production environments, suited for fast-paced startup contexts.