What are the responsibilities and job description for the Generative AI Engineer / LLM Engineer position at Connexions Data Inc?
This is a Generative AI Engineer / LLM Engineer role with a strong focus on RAG (Retrieval-Augmented Generation), NLP, and Python.
They want someone who can:
- Build Generative AI applications
- Develop RAG-based chatbots and AI assistants
- Fine-tune LLMs
- Work with Python
- Deploy AI solutions in an Agile environment
Likely experience expected:
- 8 years in AI/ML
- 2 4 years specifically in Generative AI or LLMs (depending on the market and client expectations)
Job Description
Must Have Technical/Functional Skills
Experience in executing projects in Agile Framework
Proven experience in machine learning and deep learning frameworks (e.g., TensorFlow, PyTorch).
Strong programming skills in Python and familiarity with libraries such as NumPy, Pandas, and Scikit-learn.
Experience with generative models (e.g., GANs, VAEs, Transformers) and natural language processing.
Proficiency in RAG (Retrieval-Augmented Generation) techniques.
Strong understanding of natural language processing (NLP). Experience with data preprocessing and model fine-tuning.
Familiarity with evaluation metrics for RAG systems.
Knowledge of transformer architectures and training techniques.
Awareness of ethical considerations and bias mitigation strategies.
Understanding of autonomous decision-making algorithms.
Proficiency in the programming language Python.
Strong analytical and problem-solving skills.
Roles & Responsibilities
Qualifications:
Bachelor s or master s degree in computer science, data science or equivalent
Develop and implement generative AI models using frameworks like TensorFlow and PyTorch.
Build and optimize RAG (Retrieval-Augmented Generation) pipelines.
Work on NLP tasks such as text classification, summarization, and conversational AI.
Perform data preprocessing, cleaning, and feature engineering using Python libraries (NumPy, Pandas).
Fine-tune and optimize transformer-based models and LLMs for specific use cases.
Evaluate model performance using RAG and NLP evaluation metrics.
Develop and integrate machine learning models into applications.
Apply autonomous decision-making logic in AI-driven workflows where needed.
Generic Managerial Skills, If any
Good to have Manufacturing domain understanding
Excellent communication
Team collaboration
Documentation and knowledge sharing