Sr. Generative AI Engineer

3 weeks ago

Madurai, Tamil Nadu, India MindBrain Full time

Experience 5+ yrs

Skills:


• Azure document intelligence


• Azure AI search


• Chunking and embedding,


• Agent framework like Lang graph/Autogen


• Prompting


• Python,


• Evaluation frameworks like DeelEval or Ragas using LLMs

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.

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