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Mid-level AI engineer title, Bangalore metro, and broad ML/cloud requirements create high competition.
Core ML and cloud engineering skills are transferable, regulated healthcare experience moderately increases sensitivity.
Explicit 6+ years plus mandatory ML, MLOps, and cloud toolset enforces high shortlisting strictness.
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Design, build, and maintain AI-enabled applications and backend services integrating machine learning models into production via APIs and cloud-native architectures.
Optimize AI systems for performance, reliability, and cost efficiency, including monitoring, logging, and root-cause analysis of production issues.
Build and maintain CI/CD pipelines, automate deployment processes, and collaborate with cross-functional teams for secure, compliant AI deployment.
6+ years of total engineering experience.
Proficiency in Python programming with strong software engineering practices.
Experience with cloud platforms (AWS, Azure, or GCP), containerization (Docker), and orchestration (Kubernetes).
Work Location: Bangalore; Work Mode: Hybrid (3 days in office).
Experienced in integrating and deploying real-time or low-latency ML inference systems in production environments.
Familiarity with MLOps pipelines, model lifecycle management, and enterprise or regulated environment software engineering.
Strong systems thinking, performance optimization skills, and proven ability to collaborate with Data Science and Platform teams.