Generative AI Engineer — NLP, RAG & Transformers

Tata Consultancy Services; Long Beach (CA); On-site; USD 120,000 - 150,000; Full time

Benefits offered by this job

Medical coverage

401K plan

Training & Certification reimbursement

Job summary

Tata Consultancy Services in Long Beach, CA seeks a data science professional to drive generative AI projects. You will develop models with TensorFlow/PyTorch, build RAG pipelines, and apply NLP to real-world text tasks. Strong Python skills and data preprocessing are essential.

Responsibilities include fine-tuning LLMs, evaluating model performance, and integrating ML models into applications with attention to ethics and bias mitigation.

Qualifications

• Degree in computer science, data science or equivalent.
• Experience building generative AI models with TensorFlow or PyTorch.
• Proficiency in NLP tasks, data preprocessing and model fine-tuning.
• Familiarity with RAG pipelines and evaluation metrics.

Responsibilities

• Develop and implement generative AI models using TensorFlow or PyTorch.
• Build and optimize RAG pipelines for retrieval-augmented generation.
• Work on NLP tasks such as text classification and summarization.
• Perform data preprocessing and feature engineering using Python libraries.
• Fine-tune transformer-based models and LLMs for use cases.

Skills

Agile experience

ML & DL frameworks

Python programming

NLP & GANs/Transformers

RAG techniques

Transformer architectures

Bias mitigation

Autonomous decision-making

Data preprocessing & fine-tuning

Model evaluation metrics

Education

Bachelor's or Master's in CS/Data Science

Tools

TensorFlow

PyTorch

NumPy

Pandas

Scikit-learn

Job description

Tata Consultancy Services in Long Beach, CA seeks a data science professional to drive generative AI projects. You will develop models with TensorFlow/PyTorch, build RAG pipelines, and apply NLP to real-world text tasks. Strong Python skills and data preprocessing are essential.

Responsibilities include fine-tuning LLMs, evaluating model performance, and integrating ML models into applications with attention to ethics and bias mitigation.