Back
Mid-level ML role in Bengaluru, popular title with broad MLOps/LLM requirements increases competition.
Requires specialized ML/MLOps and LLM production experience, limiting transferability outside ML-focused engineering roles.
Explicit 5+ years requirement plus extensive mandatory MLOps, cloud, and tooling skills increases filtering strictness.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Build and operate production-grade, scalable machine learning and generative AI services with APIs for enterprise products.
Develop and maintain MLOps infrastructure including CI/CD pipelines, model lifecycle automation, monitoring, and reliability systems.
Collaborate with Applied Science and Engineering teams to deploy, optimize, and secure AI applications with low latency and high availability.
5+ years experience in Machine Learning Engineering, MLOps, Platform Engineering, or Backend Engineering supporting production ML systems.
Strong programming skills in Python and at least one of Java, Go, or Scala.
Experience with deployment and operation of production machine learning services and related cloud-native technologies (Docker, Kubernetes, AWS).
Familiarity with LLM tooling (e.g. LangChain, LlamaIndex), data pipeline orchestration (e.g. Airflow, Kubeflow), API design (REST/gRPC), and monitoring platforms (e.g. Prometheus, Grafana).
Experienced in designing production ML systems focused on reliability, scalability, security, and operational excellence rather than only model development.
Skilled at collaborating cross-functionally with Applied Science, Product, and Platform teams to translate models into business impact.
Expertise in platform engineering with ability to automate workflows, build developer tools, and maintain production AI infrastructure at scale.