Strong AI Developer (ML, Agentic AI, Gen AI, Python, Java ) : Visa Independent , W2

Qualifications

• Mandatory skills* Machine Learning/Agentic AI/ GenAI: Extensive experience with Agentic AI/GenAI models, including but not limited to fine-tuning, LoRA (Low-Rank Adaptation), and RAG

• Experience with OpenAI API, Hugging Face Transformers, LangChain, or similar GenAI tools is essential

• NLP: Proven experience in text processing, metadata tagging, summarization, and entity recognition

• Good Java and Python knowledge

• Backend and API Development: Familiarity with building REST APIs to integrate Agentic AI/GenAI models within workflows

• 2 more items(s)

Responsibilities

• Design and implement core components of the AI-based application, including metadata extraction, RAG, redlining

• Develop efficient and robust backend algorithms

• Build and integrate models for key metadata extraction

• Implement RAG pipelines for handling complex queries

• Develop redlining capabilities for real-time tracking and comparison, handling extensive text

• Implement techniques to optimize model performance, focusing on computational efficiency for CPU-based infrastructure

• 3 more items(s)

More job highlights

Job description

Mandatory skills* Machine Learning/Agentic AI/ GenAI: Extensive experience with Agentic AI/GenAI models, including but not limited to fine-tuning, LoRA (Low-Rank Adaptation), and RAG.

Experience with OpenAI API, Hugging Face Transformers, LangChain, or similar GenAI tools is essential.

NLP: Proven experience in text processing, metadata tagging, summarization, and entity recognition. Good Java and...

Python knowledge

Desired skills*

Backend and API Development: Familiarity with building REST APIs to integrate Agentic AI/GenAI models within workflows.

Detailed JD *(Roles and Responsibilities)

Architecture and Development:

Design and implement core components of the AI-based application, including metadata extraction, RAG, redlining.

Develop efficient and robust backend algorithms

Metadata Extraction:

Build and integrate models for key metadata extraction.

Retrieval-Augmented Generation (RAG):

Implement RAG pipelines for handling complex queries.

Redlining and Comparison:

Develop redlining capabilities for real-time tracking and comparison, handling extensive text.

Data Processing and Optimization:

Implement techniques to optimize model performance, focusing on computational efficiency for CPU-based infrastructure.

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