Excelra

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.

📌 GenAi Engineer Excelra (Delhi)
🏢 Skyleaf Consultants
📍 Delhi