Machine Learning Engineer

What you’ll do :

• Scoping, driving, and delivering complex projects across multiple teams.
• Work with product / business owners, enterprise / application / solution architects, data scientists, engineers, and other stakeholders to scale up Big Data and data science research and development initiatives w.R.T to the industrial standards.
• Develop deterministic and machine learning models (ML) and pilot projects to find the most scalable solution to business problems (plug and play on readymade ML models and python abilities).
• He / she should be a self-starter who has a strong interest in dealing with statistics, understanding complex systems, solving challenging challenges, and organizing and presenting data and reports.
• Identifies the data needed for root cause analysis, gathers, and pulls data, analyses it to create a hypothesis, and uses data to validate it.
• Develops and maintains scalable predictive models and drive insights that can be of value to the business.
• Implement Continuous Integration / Continuous Deployment (CI / CD) pipelines for machine learning models.
• Demonstrate technical expertise and provide guidance to business stakeholders on how to gain value from the data science initiatives.
• Leverage and plug and play deep learning frameworks (e.G., TensorFlow, PyTorch) to build and optimize neural networks for various applications.
• Create, create, and apply cutting-edge AI models and algorithms to resolve challenging business issues and keep up with the most recent developments in AI research and use creative approaches in practical situations.
• Understanding of conceptual, logical, and physical architectures for cloud-based data solutions – AWS preferred.
• Provide complete frameworks and solutions for projects. Keep an eye on and measure efficacy Establish best practices and frameworks for various data and analytics landscape capabilities.
• Creates and assesses commercial and open-source data lineage tools.
• Familiarity with the logical, conceptual, and physical architectures of cloud-based data solutions;

preference is given to AWS.

Key Performance Areas :

Technical Skills :

Requirements :

Soft Skills :

Requirements :