GenAI Engineer

Role:
GenAI Developer
Location:
Toronto
- Hybrid
Hire Type:
Contract

Job Posting

Description:

Seeking for a motivated GenAI Engineer to work with our Global Advanced Customer Analytics team and develop & support solutions primarily focused on contact center BOTs.

Responsibilities:

· GenAI use cases requirements understanding working with product owners, business and
design LLM solutions aligned with approved RAG patterns

· Drive data requirements conversations and work with data office to get required data for the use case

· Design efficient chunking & indexing strategies to support answer generation as per expectations

· Develop and deploy inference APIs using frameworks like langchain and deploy to AKS or function apps

· Capable to pick up UI work as needed to support front end integration collaborating with UI team

· Support GenAI knowledge BOTs in production by monitoring user feedback and improve BOT response quality through enhancements as per feedback

· Embrace best practices, processes related to MLOps, Coding best practices

· Experience in optimizing model accuracy by efficient prompt refinements

· Collaborate with existing members in the team, contribute to reusable GenAI code base

What we are looking for:

· Minimum of years of experience solving high-impact business problems using Machine learning & latest advancements in LLMs

· Proven experience as a ML Engineer, with a strong focus on generative AI, prompt engineering, and RAG applications.

· Hands-on experience using Azure AI search, Langchain, other Vector stores. Experience in Azure OpenAI. Open-source LLMs is a plus.

· Strong software development skills with proficiency in Python, Pyspark, preferably using Databricks Azure ML. Basic knowledge of React or equivalent UI frameworks is a plus.

· Strong experience in supporting production applications post development and continuously improve response quality to increase benefits

· Strong collaboration skills, ability to translate sophisticated requirements into technical backlog, presentation skills, and an ability to balance a sense of urgency with shipping high-quality and pragmatic solutions.

· Experience in productionizing code through the Dev Ops pipeline (git, Jenkins pipeline, code scan).