Sr AIML Engineer - ONSITE

Role: Sr. AI/ML Engineer

Location: Cupertino CA (Onsite)

Duration: Long Term Contract

Job Description:

Responsibilities:

Design develop and implement RetrievalAugmented Generation (RAG) systems utilizing Large Language Models (LLMs) to enhance our AI capabilities.

Work with vector stores semantic search and other relevant technologies to optimize the performance and accuracy of our LLMbased applications.

Conduct experiments to finetune LLMs for specific use cases and improve their effectiveness in realworld applications.

Stay uptodate with the latest advancements in machine learning natural language processing and AI technologies to continually improve our methodologies and solutions.

Requirements:

Proven experience in applied machine learning with a focus on natural language processing and Large Language Models (LLMs).

Strong understanding of RetrievalAugmented Generation (RAG) and its implementation using LLMs.

Proficiency with vector stores semantic search and related technologies.

Experience with machine learning frameworks such as TensorFlow PyTorch or similar.

Knowledge of finetuning techniques for LLMs to adapt them to specific tasks and domains.

Strong programming skills in Python and familiarity with libraries/APIs such as Hugging Face Transformers Langchain llamIndix OpenAI or similar.

Preferred Qualifications:

Experience with cloud platforms such as AWS or Google Cloud.

Understanding of MLOps practices for deploying and maintaining machine learning models in production.

Familiarity with other AI and ML technologies such as reinforcement learning computer vision or recommendation systems is a plus.

Familiarity with MLX framework or training/inferencing on apple silicon is a plus

Any Web development understanding or experience with using RAG in any web application is a big positive.

Role: Sr. AI/ML Engineer Location: Cupertino, CA (Onsite) Duration: Long Term Contract Job Description: Responsibilities: Design, develop, and implement Retrieval-Augmented Generation (RAG) systems utilizing Large Language Models (LLMs) to enhance our AI capabilities. Work with vector stores, semantic search, and other relevant technologies to optimize the performance and accuracy of our LLM-based applications. Conduct experiments to fine-tune LLMs for specific use cases and improve their effectiveness in real-world applications. Stay up-to-date with the latest advancements in machine learning, natural language processing, and AI technologies to continually improve our methodologies and solutions. Requirements: Proven experience in applied machine learning with a focus on natural language processing and Large Language Models (LLMs). Strong understanding of Retrieval-Augmented Generation (RAG) and its implementation using LLMs. Proficiency with vector stores, semantic search, and related technologies. Experience with machine learning frameworks such as TensorFlow, PyTorch, or similar. Knowledge of fine-tuning techniques for LLMs to adapt them to specific tasks and domains. Strong programming skills in Python and familiarity with libraries/APIs such as Hugging Face Transformers, Langchain, llamIndix, OpenAI, or similar. Preferred Qualifications: Experience with cloud platforms such as AWS or Google Cloud. Understanding of MLOps practices for deploying and maintaining machine learning models in production. Familiarity with other AI and ML technologies such as reinforcement learning, computer vision, or recommendation systems is a plus. Familiarity with MLX framework or training/inferencing on apple silicon is a plus Any Web development understanding or experience with using RAG in any web application is a big positive.