Gen AI Engineer

Role Overview: You will be responsible for designing and implementing solutions in the areas of AI/ML, specifically focusing on developing models using Machine Learning and Deep Learning technologies. Your expertise in Google Cloud Platform (GCP) and various Python libraries will be crucial for this role. Key Responsibilities: - Minimum 5 years of experience in Machine Learning and predictive analytics. - Hands-on experience in developing models using technologies such as Keras, TensorFlow, pyTorch, and GCP AI/ML services. - Proficiency in Python and Spark, with experience in libraries like TensorFlow, Spark ML, scikit-learn, pandas, and NumPy. - Strong software engineering skills in Python, including the ability to build libraries and deploy Python codes in production. - Experience in model deployment and monitoring on public clouds (AWS/Azure/GCP), with GCP experience being an added advantage. - Previous exposure to handling Supervised Learning, Unsupervised Learning, and Reinforcement Learning problems across various industry verticals. - Ability to transform data science prototypes into scalable products for seamless production use. - Familiarity with implementing CI/CD principles in the Machine Learning domain (ML Ops). - Knowledge of containers and orchestration (specifically Kubernetes). - Proficiency in Google Cloud Platform services such as BigQuery, Cloud Composer, Vertex AI, and AI/ML services in general. - Strong grasp of Statistics concepts and expertise in Machine Logs processing, text mining, and text analytics. Qualification Required: No specific qualifications are mentioned in the job description. Role Overview: You will be responsible for designing and implementing solutions in the areas of AI/ML, specifically focusing on developing models using Machine Learning and Deep Learning technologies. Your expertise in Google Cloud Platform (GCP) and various Python libraries will be crucial for this role. Key Responsibilities: - Minimum 5 years of experience in Machine Learning and predictive analytics. - Hands-on experience in developing models using technologies such as Keras, TensorFlow, pyTorch, and GCP AI/ML services. - Proficiency in Python and Spark, with experience in libraries like TensorFlow, Spark ML, scikit-learn, pandas, and NumPy. - Strong software engineering skills in Python, including the ability to build libraries and deploy Python codes in production. - Experience in model deployment and monitoring on public clouds (AWS/Azure/GCP), with GCP experience being an added advantage. - Previous exposure to handling Supervised Learning, Unsupervised Learning, and Reinforcement Learning problems across various industry verticals. - Ability to transform data science prototypes into scalable products for seamless production use. - Familiarity with implementing CI/CD principles in the Machine Learning domain (ML Ops). - Knowledge of containers and orchestration (specifically Kubernetes). - Proficiency in Google Cloud Platform services such as BigQuery, Cloud Composer, Vertex AI, and AI/ML services in general. - Strong grasp of Statistics concepts and expertise in Machine Logs processing, text mining, and text analytics. Qualification Required: No specific qualifications are mentioned in the job description.