Job Description

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

----------------------------

Enable the workforce to adopt an AI first strategy by leveraging AI code assistance tools

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 we're looking for

--------------------------

2+ 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

---------------------------------------

Enable the workforce to adopt an AI first strategy by leveraging AI code assistance tools

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.