Data Research Engineer - AI/ML

at NextHire

Job description

Job Title: Data Research Engineer – AI/ML

Location: Remote

Experience Required: Minimum 4 years

Role Summary:

We are looking for a Data Research Engineer with strong expertise in Artificial Intelligence (AI), Large Language Models (LLMs), Natural Language Processing (NLP), and deep learning frameworks such as TensorFlow and PyTorch. The ideal candidate will have hands-on experience building or contributing to AI/ML solutions, particularly involving LLM-based architectures such as RAG and tools like LangChain.

This role will involve researching, designing, and deploying intelligent models and solutions in real-world scenarios, while working closely with data scientists and product teams.

Key Responsibilities:

Build, fine-tune, and optimize LLM-based and NLP solutions for various real-world use cases

Research and implement state-of-the-art retrieval-augmented generation (RAG) pipelines and applications

Work with tools such as LangChain, HuggingFace, TensorFlow, and PyTorch

Design scalable pipelines to train, evaluate, and deploy machine learning models

Collaborate with data engineers and software developers to integrate AI solutions into production systems

Write clean, efficient, and well-documented Python and SQL code

Stay current with emerging research in AI, ML, and NLP and translate academic findings into applied use

Required Skills & Experience:

4+ years of hands-on experience in Python programming for data and ML projects

Strong experience in AI/ML frameworks such as TensorFlow, PyTorch, and Scikit-learn

Proficient in SQL for data extraction, transformation, and analysis

Practical knowledge of LLMs, NLP, and transformer-based models

Exposure to RAG pipelines, LangChain, or similar LLM orchestration tools

Experience deploying or integrating LLMs in real-world use cases is highly preferred

Nice to Have:

Contributions to open-source AI/ML projects

Understanding of cloud platforms (GCP, AWS, Azure) for ML deployment

Familiarity with data labeling tools, MLOps workflows, or API deployment (e.g., FastAPI)

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