MLOps Engineer

Responsibilities :

Strong Python programming Hands-on experience in Machine Learning workflows Experience with MLOps tools: MLflow / Kubeflow / Airflow / SageMaker / Vertex AI Knowledge of CI/CD tools (GitHub Actions, Jenkins, etc.) Experience deploying models using: Docker, REST APIs, Flask/FastAPI Understanding of: Model versioning Experiment tracking Model monitoring

Additional Responsibilities:

Experience with real-time inference systems Exposure to monitoring tools (Prometheus, Grafana) Knowledge of LLM deployment / RAG pipelines

Technical and Professional Requirements:

Exposure to cloud platforms (AWS / Azure / GCP) for ML deployment Knowledge of LLMOps / GenAI deployment pipelines Familiarity with: Feature stores Data pipelines (Spark, Kafka) Experience with Kubernetes (basic deployment level)