Machine Learning (ML)

Role & responsibilities

Project Role : AI / ML Engineer
Project Role Description : Develops applications and systems that utilize AI tools, Cloud AI services, with proper cloud or on-prem application pipeline with production ready quality. Be able to apply GenAI models as part of the solution. Could also include but not limited to deep learning, neural networks, chatbots, image processing.
Must have skills : Machine Learning (ML)
Good to have skills : NA
Minimum 5 year(s) of experience is required
Educational Qualification : 15 years full time education

Summary:
As an AI / Machine Learning Engineer, a typical day involves designing and building advanced applications and systems that integrate artificial intelligence tools and cloud-based AI services. The role requires managing the entire application pipeline, whether deployed on cloud platforms or on-premises, ensuring solutions are production-ready and scalable. The engineer actively incorporates generative AI models into projects and works on diverse technologies such as deep learning, neural networks, chatbots, and image processing. Collaboration with various teams to align technical solutions with project goals is a key aspect of daily activities, fostering innovation and efficiency in AI-driven development.

Roles & Responsibilities:

• Expected to be an SME, collaborate and manage the team to perform.
• Responsible for team decisions.
• Engage with multiple teams and contribute on key decisions.
• Provide solutions to problems for their immediate team and across multiple teams.
• Lead the design and implementation of AI and machine learning solutions to meet project requirements.
• Mentor junior team members to enhance their technical skills and understanding of AI technologies.
• Coordinate cross-functional efforts to ensure seamless integration of AI components within larger systems.

Professional & Technical Skills:

• Must To Have Skills: Proficiency in Machine Learning (ML).
• Strong knowledge of deep learning frameworks and neural network architectures.
• Experience with cloud AI services and deployment pipelines for production environments.
• Familiarity with generative AI models and their practical applications.
• Ability to develop and optimize chatbots and image processing algorithms.
• Skilled in designing scalable AI systems that integrate with both cloud and on-premises infrastructures.