MLOps Engineer

Job description

Educational Requirements - Bachelor of Engineering 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) Preferred Skills - Analytics- >Data Science - Technology- >AI-Data science- >Data Science - Basic - Technology- >AI-Generative AI- >Generative AI - Basic