Machine Learning Engineer

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 .