AI / ML Engineer

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 : Large Language Models (LLMs) Good to have skills : NA Minimum 12 year(s) of experience is required Educational Qualification : 15 years full time education

Summary:

As an AI / ML Engineer, a typical day involves designing and building advanced applications and systems that leverage artificial intelligence technologies. This role requires integrating cloud-based or on-premises pipelines to ensure solutions are production-ready and scalable. The work often includes applying generative artificial intelligence models to enhance system capabilities. Additionally, the role may involve exploring various domains such as deep learning, neural networks, conversational agents, and image analysis to create innovative and efficient AI-driven solutions. Collaboration and continuous improvement are key aspects of daily activities in this position.

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.
• Expected to provide solutions to problems that apply across multiple teams.
• Lead the development and deployment of AI models ensuring alignment with project goals and timelines.
• Facilitate knowledge sharing and mentorship within the team to foster professional growth and technical excellence.
• Coordinate cross-functional efforts to integrate AI solutions seamlessly into existing systems and workflows.

Professional & Technical Skills:

• Must To Have Skills: Proficiency in Large Language Models (LLMs).
• Experience with designing and implementing scalable AI pipelines in cloud or on-premises environments.
• Strong understanding of generative artificial intelligence models and their practical applications.
• Familiarity with deep learning architectures, neural networks, and their deployment in production systems.