Gen AI Engineer

Job Description

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.
• 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.

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.