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 .