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