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Protocol Intelligence
Data-driven signals on your job's competitivenessHybrid/remote hiring, mid-senior level, and metro locations increase applicant density.
Role requires specialized MLOps, cloud, and ML platform expertise, limiting cross-industry transferability.
Multiple mandatory tools and explicit 6-10 years MLOps experience make filters highly strict.
Job Description
Structured overview of role & requirementsAbout This Role
Design and manage end-to-end machine learning lifecycle pipelines including training, deployment, and monitoring.
Implement and maintain scalable, secure, and compliant MLOps platforms with CI/CD, DevSecOps, and Infrastructure as Code practices.
Monitor and optimize ML infrastructure for performance, scalability, reliability, and cost efficiency in production environments.
Minimum Requirements
6-10 years of experience with at least 4 years hands-on in MLOps or ML lifecycle management.
Strong proficiency in Python and experience with MLflow or Kubeflow, AWS SageMaker, AWS Bedrock, ECS, Docker, CI/CD, and Infrastructure as Code (Terraform or CloudFormation).
Experience in operationalizing ML solutions from Proof of Concept to Production including model versioning, registry, monitoring, and lifecycle management.
Work mode: Hybrid/Remote with quarterly 5 days in office for remote employees; Location: Trivandrum/Kochi/Remote.
Ideal Candidate Profile
Deep experience in building production-grade ML/AI platforms and managing ML workflows in cloud environments, especially AWS.
Familiarity with DevSecOps, security governance, model monitoring, observability, and automation in ML production systems.
Experience working collaboratively with Data Engineering, Data Science, DevOps, and application teams to deliver scalable and compliant ML solutions.