MLOps Engineer

Job description

Educational Requirements Bachelor of Engineering Service Line Data Analytics Unit 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