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We are seeking a highly skilled Machine Learning Engineer with a strong MLOps background to join our team. You will play a pivotal role in building and scaling our machine learning models from development to production. Your expertise in both machine learning and operations will be essential in creating efficient and reliable ML pipelines.
Job Responsibility:
Collaborate with data scientists to develop, train, and evaluate machine learning models
Build and maintain MLOps pipelines, including data ingestion, feature engineering, model training, deployment, and monitoring
Leverage cloud platforms (AWS, GCP, Azure) for ML model development, training, and deployment
Implement DevOps/MLOps best practices to automate ML workflows and improve efficiency
Develop and implement monitoring systems to track model performance and identify issues
Conduct A/B testing and experimentation to optimize model performance
Work closely with data scientists, engineers, and product teams to deliver ML solutions
Stay updated with the latest trends and advancements
Requirements:
Solid foundation in machine learning algorithms and techniques
Experience in MLOps practices and tools (e.g., MLflow, Kubeflow, Airflow)
Experience in DevOps tools (e.g., Docker, Kubernetes, CI/CD)
Experience in GenAI/LLM
Proficiency in Python and relevant ML libraries (e.g., TensorFlow, PyTorch, Scikit-learn)
Outstanding analytical and problem-solving skills
Ability to learn quickly
Good communication and interpersonal skills
Any degree with 5-9 years of experience in Computer Science, IT or related field
Excellent analytical and troubleshooting skills
Strong verbal and written communication skills
Ability to work effectively with global, virtual teams
High degree of initiative and self-motivation
Ability to manage multiple priorities successfully
Team-oriented, with a focus on achieving team goals
Ability to learn quickly, be organized and detail oriented
Strong presentation and public speaking skills
Nice to have:
Experience with big data technologies (e.g., Spark, Hadoop), and performance tuning in query and data processing
Experience with data engineering and pipeline development
Experience in statistical techniques and hypothesis testing, experience with regression analysis, clustering and classification
Knowledge of NLP techniques for text analysis and sentiment analysis
Experience in analyzing time-series data for forecasting and trend analysis