Role Overview :
We are looking for an experienced MLOps Engineer who will bridge AI / ML model development with production deployment and monitoring , ensuring models are scalable, reliable, and performant in production environments.
Key Responsibilities :
- Build and manage MLOps pipelines for AI / ML models (segmentation, Next Best Action, recommendations).
- Deploy and monitor models across Dev / SIT / PROD environments .
- Implement model versioning, drift detection, and retraining workflows .
- Optimize model inference performance for low-latency responses.
- Collaborate with infrastructure teams to ensure hardware and cloud readiness for GenAI .
- Establish observability and logging for model behavior and system performance.
Required Skills :
7–10 years of experience in MLOps or related roles.Strong knowledge of ML lifecycle management , CI / CD for ML , and model deployment frameworks (e.g., MLflow, Kubeflow, Airflow).Expertise in Python , Docker , and Kubernetes .Experience with cloud platforms (AWS / Azure / GCP) and GPU-based infrastructure.Familiarity with monitoring tools (Prometheus, Grafana) and logging systems .Understanding of model drift detection and retraining strategies .Good to Have :
Exposure to GenAI model deployment .Experience with data pipelines and feature stores .