Generative AI Engineer - Data Modeling (6-10 yrs)

The Candidate should we exp in below skill :

1. Agent Architecture

2. Graph RAG knowledge

3. Any work on multi agent Architecture

4. Types of RAG Awareness

5. Ever worked on fine tuning a model?

6. Transformer architecture? Multi headed attention concepts

The ideal candidate should be working on GenAI projects from last 2 years and minimum 6 months in Langchain, RAG, fine tuning models and agents.

Position Overview :

As a Generative AI Engineer at Relevance Lab, you will be responsible for developing and implementing applications using large language models (LLMs). Your expertise in Python and LangChain will be essential in creating scalable, efficient, and innovative AI solutions. You will work closely with cross-functional teams to design, develop, and deploy state-of-the-art AI systems.

Key Responsibilities :

- Develop and implement AI applications using large language models (LLMs) with Python and LangChain.

- Design, build, and optimize machine learning algorithms for generative AI applications.

- Collaborate with data scientists, software engineers, and product managers to create robust AI-driven applications.

- Conduct research to stay updated with the latest advancements in generative AI and LLMs.

- Analyze and preprocess large datasets to ensure high-quality input for model training.

- Evaluate and fine-tune models to achieve optimal performance and accuracy.

- Write clean, maintainable, and efficient code adhering to best practices.

- Participate in code reviews and provide constructive feedback to team members.

- Document technical specifications, processes, and model performance metrics.

Qualifications :

- Bachelor's or Master's degree in Computer Science, Engineering, or a related field.

- Proven experience in Python programming and developing AI models.

- Strong expertise in Lang Chain and its applications in generative AI.

- Solid understanding of large language models (LLMs) and their implementation.

- Experience with deep learning frameworks such as TensorFlow or PyTorch.

- Familiarity with natural language processing (NLP) techniques and tools.

- Ability to work with large datasets and perform data preprocessing and analysis.

- Excellent problem-solving skills and a proactive approach to challenges.

- Strong communication skills and ability to work effectively in a collaborative team environment.