AI Engineer

AI Engineer

Client: UST/Applied Materials

Location:

Onsite - Santa Clara, CA (Local profile only)

Mandatory Areas Must Have Skills

Skill 1 – Demonstrate advanced programming expertise, particularly in Python, with deep proficiency in AI-centric libraries such as Tensor Flow and PyTorch.

Skill 2 – Design, develop, and deploy AI agents capable of autonomous decision-making and task execution using LLMs and multi-modal models.

Skill 3 – Hands-on experience with RAG architectures, including document chunking, embedding generation, and retrieval systems.

About the Role:

We are seeking a highly skilled and innovative AI Engineer to join our team and lead the development of intelligent AI agents and RAG-based applications. This role is ideal for someone passionate about pushing the boundaries of applied AI, with hands-on experience in building scalable, production-grade GenAI systems.

Key Responsibilities:

• Demonstrate advanced programming expertise, particularly in Python, with deep proficiency in AI-centric libraries such as Tensor Flow and PyTorch.

• Architect and implement Retrieval-Augmented Generation (RAG) pipelines to enhance model performance using external knowledge sources.

• Design, develop, and deploy AI agents capable of autonomous decision-making and task execution using LLMs and multi-modal models.

• Implement and manipulate complex algorithms essential for developing and optimizing generative AI models.

• Manage data pipelines involving data pre-processing, augmentation, and synthetic data generation to enhance model training and performance.

• Ensure robust data handling practices including cleaning, labeling, and structuring datasets for generative AI workflows.

Required Qualifications:

• Bachelor's or master's degree in computer science, AI/ML, or related field.

• 3+ years of experience in AI/ML engineering, with at least 1 year focused on Generative AI.

• Hands-on experience with RAG architectures, including document chunking, embedding generation, and retrieval systems.

• Proficiency in Python and familiarity with libraries such as Hugging Face, Transformers, OpenAI API, and PyTorch or Tensor Flow.

• Experience with cloud platforms (AWS, Azure, GCP) and containerization (Docker, Kubernetes).

• Strong understanding of LLM capabilities, limitations, and prompt engineering techniques.

Preferred Qualifications:

• Experience with fine-tuning LLMs or training custom models.

• Familiarity with multi-modal AI (text, image, audio).

• Contributions to open-source GenAI projects or publications in AI conferences.

• Experience with CI/CD pipelines for ML model deployment.