MLOps Engineer

• BS, MS, or PhD degree in Computer Science or a related field, or equivalent practical experience. • 3 to 5 year of experience and Hands-on with Languages : Scala, Java , Python • Strong computer science fundamentals: data structures, algorithms, performance complexity, and implications of computer architecture on software performance (e.g., I/O and memory tuning). • Solid software engineering fundamentals: experience with version control systems (Git, GitHub) and workflows, and the ability to write production-ready code. • Knowledge of Machine Learning or Data Science languages, tools, and frameworks, including SQL, Scikit-learn, NLTK, NumPy, Pandas, TensorFlow, and Keras. • Understanding of machine learning techniques (e.g., classification, regression, clustering) and principles (e.g., training, validation, and testing). • Experience with data processing tools and distributed computing systems and related technologies such as Spark, Hive, and Flink. • Familiarity with cloud technologies, including AWS SageMaker tools and AWS Bedrock. • Understanding of DevOps concepts, including CI/CD. • Experience with software container technology, such as Docker and Kubernetes. • In-depth knowledge of MLOps principles and tools for model lifecycle management, including experiment tracking, model registry, and serving infrastructure. • Experience with workflow orchestration tools (e.g., Apache Airflow, Kubeflow Pipelines). • Familiarity with model explainability (XAI) and fairness techniques. • Proficiency in optimizing machine learning models for performance, efficiency, and resource utilization. • Experience with A/B testing frameworks and statistical analysis for model evaluation.