Position: AI Engineer

Location: Santa Clara, CA***Day 1 Onsite***

Duration: 1 Years

AI Engineer all profile rejected need more

With good experience in LLL

PhD on ML, Data science and LLM

Mandatory Areas

Must Have Skills

Skill 1 Demonstrate advanced programming expertise, particularly in Python, with deep proficiency in AI-centric libraries such as TensorFlow 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

Good To have Skills

Skill 1 Familiarity with multi-modal AI (text, image, audio)..

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.

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