GenAi Engineer
Excelra
Apply  

Highlights:

4.00 - 11.00 Years
20.00 - 25.00 INR (Lacs)/Yearly
Full-time
Delhi, Bengaluru, Hyderabad

Roles & Responsibility

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.

 

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.

Requirements

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.

 

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.