Senior Gen AI Engineer : RAG Systems And AI Transformation

Summary
We are seeking a highly skilled and forward:thinking GenAI Engineer to join our AI innovation team. This role is ideal for someone with deep technical expertise in Generative AI, a strong foundation in Python programming, and a passion for driving enterprise AI transformation.
You will be instrumental in designing, developing, and deploying advanced Retrieval:Augmented Generation (RAG) systems. You 'll also play a pivotal role in enabling our internal workforce to embrace and adopt AI technologies.
Your role in our mission
typedisc:
:
Architect and implement scalable RAG systems using Python and modern GenAI tools.
:
Build custom pipelines for document ingestion, chunking strategies, and embedding generation. Working knowledge in LlamaIndex is preferable.
:
Have a deep knowledge in using AI augmented tools like GitHub Copilot. Experience in developing custom extensions
:
Evaluate and implement different embedding models (OpenAI, Azure OpenAI, Cohere, etc.) and chunking strategies (fixed:size, semantic:aware, overlap:based).
:
Create and optimize indexing strategies (vector, hybrid, keyword:based, hierarchical) for performance and accuracy.
:
Work with Azure AI Services, particularly Azure Cognitive Search and OpenAI integration, to deploy end:to:end AI applications.
:
Collaborate closely with cross:functional teams including data engineers, product managers, and domain experts.
:
Conduct AI enablement sessions, workshops, and hands:on labs to upskill internal teams on GenAI usage and best practices.
:
Participate in code reviews, contribute to best practices, and ensure the reliability, scalability, and maintainability of AI systems.
What were looking for
typedisc:
:
8 years of experience in software engineering, with strong expertise in Python.
:
Proven track record of building and deploying RAG:based GenAI solutions.
:
Hands:on experience with LlamaIndex, LangChain, or equivalent frameworks.
:
Familiarity with prompt engineering, prompt tuning, and managing custom Copilot extensions.
:
Strong understanding of LLMs, vector databases (like FAISS, Pinecone, Azure Cognitive Search), and embedding techniques.
:
Solid knowledge of Azure AI, cloud deployment, and enterprise integration strategies.
:
Proficiency with version control and collaborative development using GitHub.
What you should expect in this role
typedisc:
:
Architect and implement scalable RAG systems using Python and modern GenAI tools.
:
Build custom pipelines for document ingestion, chunking strategies, and embedding generation. Working knowledge in LlamaIndex is preferable.
:
Have a deep knowledge in using AI augmented tools like GitHub Copilot. Experience in developing custom extensions
:
Evaluate and implement different embedding models (OpenAI, Azure OpenAI, Cohere, etc.) and chunking strategies (fixed:size, semantic:aware, overlap:based).
:
Create and optimize indexing strategies (vector, hybrid, keyword:based, hierarchical) for performance and accuracy.
:
Work with Azure AI Services, particularly Azure Cognitive Search and OpenAI integration, to deploy end:to:end AI applications.
:
Collaborate closely with cross:functional teams including data engineers, product managers, and domain experts.
:
Conduct AI enablement sessions, workshops, and hands:on labs to upskill internal teams on GenAI usage and best practices.
:
Participate in code reviews, contribute to best practices, and ensure the reliability, scalability, and maintainability of AI systems.
Job Type: Permanent