Ai ML Engineer

Must have skills : Large Language Models (LLMs), Python Frameworks, Generative AI, Prompt Engineering, Vector databases, RAG architecture, Agentic library, Lang chain/Lang graph/Autogen, Computer Vision

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 solve complex problems, developing deep learning and neural network architectures, and creating intelligent systems such as chatbots and image processing tools. Collaboration with cross-functional teams to align technical solutions with project goals is a key part of daily activities, ensuring innovative and efficient AI-driven outcomes.

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-driven solutions that meet project requirements and quality standards.
- Mentor junior team members to enhance their technical skills and support their professional growth.
- Coordinate with stakeholders to gather requirements and translate them into effective AI models and applications.

Professional & Technical Skills: 
- Must To Have Skills: Proficiency in Large Language Models (LLMs), Prompt Engineering, Python Frameworks, Generative AI, Vector databases, RAG architecture, Agentic library, Lang chain/Lang graph/Autogen, Computer Vision.
- Strong experience in developing and deploying AI models in cloud or on-premises environments.
- In-depth knowledge of neural networks, deep learning techniques, and their practical applications.
- Ability to optimize AI pipelines for performance, scalability, and maintainability.
- Familiarity with integrating generative AI models into production systems.
- Experience with data preprocessing, model training, evaluation, and fine-tuning for various AI tasks.