Gen AI Engineer

Job Description

Gen AI Engineer

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 preprocessing 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.

• 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.

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.

• 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.

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.

Disclaimer : This job posting has been aggregated from external source. Role detailscontentand availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.