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Tier-2 employer and metro location but specialized MLOps role reduces applicant density to medium.
Core MLOps skills are transferable but manufacturing (MES/LIMS/ERP) domain experience increases sensitivity to medium.
Explicit 7–9 years plus many mandatory MLOps and DevOps tool requirements makes shortlisting strict (high).
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Lead implementation and optimization of end-to-end ML pipelines using MLflow, Kubeflow Pipelines, and TFX for automated model lifecycle management.
Integrate DevOps automation using Terraform, AWS CloudFormation, Ansible, and CI/CD tools to enable scalable ML infrastructure deployment.
Monitor ML system performance with tools like Prometheus, Grafana, and ELK Stack to ensure reliability and rapid issue resolution.
7–9 years of experience in data science, machine learning, and AI.
Advanced proficiency in ML Ops tools: MLflow, Kubeflow Pipelines, TFX, and Metaflow.
Advanced proficiency in DevOps and automation tools: Terraform, AWS CloudFormation, Ansible, Jenkins, GitLab CI/CD, CircleCI, GitHub Actions, and Python scripting.
Advanced skills in monitoring/logging tools (Prometheus, Grafana, ELK Stack, Fluentd) and version control systems (Git, GitHub, GitLab, Bitbucket).
Experienced in handling manufacturing plant data integrations (MES, LIMS, ERP) within digital thread ecosystems.
Capable of designing scalable, real-time data science solutions connecting upstream/downstream systems in industrial environments.
Strong technical leadership in cross-functional collaboration including feasibility studies, process compliance, and stakeholder communication.