Previous
AI Engineer
Next
| Ref No.: |
25-01390 |
| Location: |
Santa Clara, California
|
| Position Type: | Contract |
Role name: AI Engineer
Work site: Santa Clara, CA (Onsite)
Contract Role
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 TensorFlow 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 TensorFlow.
- 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.
|