Data Science / ML Engineer

As an AI/ML Engineer at our company, your role will involve the following key responsibilities: - Comprehend business issues: You will be expected to propose valuable business solutions using statistical and AI/ML models. - Model Design: Design statistical models and machine learning/deep learning models to address complex business challenges. - Model Deployment: Develop and deploy ML/DL models into production environments. - Data Insights: Formulate available data sources, augment them, and extract valuable insights using SQL and efficient query writing techniques. - Data Visualization: Create innovative data visualizations and graphs using tools like d3js, dashplotly, and neo4j. - Integration: Ensure seamless integration of AI/ML models into business processes using cloud platforms like AWS or Azure. - Cross-Functional Collaboration: Work closely with cross-functional teams, translating business needs into actionable AI solutions. - Agile Participation: Participate in agile project delivery, ensuring the timely execution of deliverables. The qualification required for this role includes proficiency in the following technical skills: - Python Programming: Proficient in Python programming. - Statistical Knowledge: Practical knowledge of Statistics and Operations Research methods. - Frameworks and Tools: Hands-on experience with frameworks and tools such as Flask, PySpark, PyTorch, Tensor Flow, Keras, Databricks, OpenCV, Pillow/PIL, Streamlit, and neo4j. - Analytics/AI-ML Services: Expertise in Analytics/AI-ML AWS services, including Sage Maker, Canvas, Bedrock. - Predictive Modeling: Strong understanding of predictive modeling techniques like regression models, XGBoost, random forests, GBM, Neural Nets, SVM, etc. - NLP Techniques: Proficient in NLP techniques like RNN, LSTM, and Attention-based models, with knowledge of Stanford NLP, IBM, Azure, and OpenAI NLP models. - MLOps: Experience in MLOps: deploying models into production on AWS or Azure cloud platforms. - Version Control: Working knowledge of version control tools such as Git Hub and Bitbucket. In addition to technical skills, soft skills will also play a crucial role: - Analytical Abilities: Solid analytical and problem-solving abilities. - Communication Skills: Excellent communication, listening, and probing skills. - Interpersonal Skills: Robust interpersonal skills, with the ability to collaborate and work effectively within a team. It would be valuable to have an AWS Specialty Certification in Data Analytics or Machine Learning for this role. As an AI/ML Engineer at our company, your role will involve the following key responsibilities: - Comprehend business issues: You will be expected to propose valuable business solutions using statistical and AI/ML models. - Model Design: Design statistical models and machine learning/deep learning models to address complex business challenges. - Model Deployment: Develop and deploy ML/DL models into production environments. - Data Insights: Formulate available data sources, augment them, and extract valuable insights using SQL and efficient query writing techniques. - Data Visualization: Create innovative data visualizations and graphs using tools like d3js, dashplotly, and neo4j. - Integration: Ensure seamless integration of AI/ML models into business processes using cloud platforms like AWS or Azure. - Cross-Functional Collaboration: Work closely with cross-functional teams, translating business needs into actionable AI solutions. - Agile Participation: Participate in agile project delivery, ensuring the timely execution of deliverables. The qualification required for this role includes proficiency in the following technical skills: - Python Programming: Proficient in Python programming. - Statistical Knowledge: Practical knowledge of Statistics and Operations Research methods. - Frameworks and Tools: Hands-on experience with frameworks and tools such as Flask, PySpark, PyTorch, Tensor Flow, Keras, Databricks, OpenCV, Pillow/PIL, Streamlit, and neo4j. - Analytics/AI-ML Services: Expertise in Analytics/AI-ML AWS services, including Sage Maker, Canvas, Bedrock. - Predictive Modeling: Strong understanding of predictive modeling techniques like regression models, XGBoost, random forests, GBM, Neural Nets, SVM, etc. - NLP Techniques: Proficient in NLP techniques like RNN, LSTM, and Attention-based models, with knowledge of Stanford NLP, IBM, Azure, and OpenAI NLP models. - MLOps: Experience in MLOps: deploying models into production on AWS or Azure cloud platforms. - Version Control: Working knowledge of version control tools such as Git Hub and Bitbucket. In addition to technical skills, soft skills will also play a crucial role: - Analytical Abilities: Solid analytical and problem-solving abilities. - Communication Skills: Excellent communication, listening, and probing skills. - Interpersonal Skills: Robust interpersonal skills, with the ability to collaborate and work effectively within a team