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Mid-level ML-Ops, metro Bangalore location and common skillset drive medium competition.
MLOps skills transfer across industries but require ML-specific experience, so medium sensitivity.
Explicit 3–5 years plus mandatory ML deployment, cloud and infra skills create medium shortlisting strictness.
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Own and enhance machine learning platform features and subsystems, focusing on deployment, monitoring, and scaling of ML models in production.
Build and maintain tooling and interfaces for ML researchers, engineers, and data teams to streamline workflows and job orchestration.
Collaborate with cross-functional teams including infrastructure, research, and product to provision resources, automate workflows, and support ML platform adoption.
3-5 years of experience in ML-Ops, data engineering, backend development, or DevOps.
Experience deploying or maintaining ML models in real-world applications.
Proficiency in Python scripting and software engineering best practices (version control, testing).
Familiarity with cloud platforms (AWS preferred) and containerized environments like Docker.
Experience operating in ML-Ops or related technical roles with hands-on involvement in pushing research models to production.
Comfortable in collaborative, cross-functional team environments with US and India-based teams.
Ability to manage hybrid cloud infrastructure and automate workflows, signaling operational focus and platform advocacy.