Senior / Staff MLOps Engineer
Experience Level: 4 – 10 Years
Employment Type: Full time
Location: Hybrid / Remote / Onsite

Role Purpose
Models quietly decay over time due to data drift and concept drift. As a Senior MLOps Engineer, you treat ML models as living software systems, creating automated pipelines for training, continuous deployment, health monitoring, drift detection, and automated rollback.
Key Responsibilities • Design and implement robust CI/CD and GitOps workflows tailored specifically for ML artifacts and pipelines (Argo Workflows, GitHub Actions, GitLab CI).
Establish end-to-end model observability, telemetry, and automated alerting for data drift, concept drift, and performance degradation (using Evidently AI, Whylabs, Prometheus, Grafana).
Maintain model registries, experiment tracking systems, and feature stores (MLflow, Kubeflow, Feast).




Automate scheduled retraining loops and protected rollout strategies (Canary, Blue/Green, Shadow deployments).
Manage incidents, establish SLIs/SLOs for model endpoints, and lead post-mortems for production failures.

Qualifications & Requirements
Relevant Experience: 4–10 years combining DevOps, Site Reliability Engineering (SRE), and specialized Machine Learning operational tooling.
Core Technical Stack: Kubernetes, Docker, Terraform, MLflow/Kubeflow, Python, Linux scripting, Prometheus/Grafana, AWS/GCP cloud platforms.
Operational Discipline: Hands-on experience with production monitoring, incident response, and SLA enforcement for predictive microservices.

📌 Senior Mlops Engineer Bengaluru
🏢 Recro
📍 Bengaluru