Key Responsibilities :

- Designing and Developing AI models : This includes creating architectures, algorithms, and frameworks for generative AI.

- Implementing AI models : This involves building and integrating AI models into existing systems and applications.

- Working with LLMs and other AI technologies : This includes using tools and techniques like LangChain, Haystack, and prompt engineering.

- Data pre-processing and analysis : This involves preparing data for use in AI models.

- Collaborating with other teams : This includes working with data scientists, product managers, and other stakeholders.

- Testing and deploying AI models : This involves evaluating model performance and deploying them to production environments.

- Monitoring and optimizing AI models : This involves tracking model performance, identifying issues, and optimizing models for better results.

- Staying up to date with the latest advancements in Gen AI : This includes learning about new techniques, models, and frameworks.

Required Skills :

- Strong programming skills in Python : Python is the preferred language for AI development.

- Knowledge of Generative AI, NLP, and LLMs : This includes understanding the principles behind these technologies and how to use them effectively.

- Experience with RAG pipelines and vector databases : This includes understanding how to build and use retrieval-augmented generation pipelines.

- Familiarity with AI frameworks and libraries : This includes knowledge of frameworks like LangChain, Haystack, and open-source libraries.

- Understanding of prompt engineering and tokenization : This includes understanding how to optimize prompts and manage tokenization.

- Experience in integrating and fine-tuning AI models : This includes knowledge of deploying and maintaining AI models in production environments.

- Excellent communication and problem-solving skills : This includes the ability to communicate complex technical concepts to non-technical stakeholders.

Optional Skills :

- Experience with cloud computing platforms (GCP, AWS, Azure) : This can be helpful for deploying and managing AI models.

- Familiarity with MLOps practices : This can help with building and deploying AI models in a scalable and reliable manner.

- Experience with DevOps practices : This can help with automating the development and deployment of AI models.