Machine learning & Generative AI Developer

JD: Working Location: Toronto, Hybrid 3 days a week

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 Manulife 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 5 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 DevOps pipeline (git, Jenkins pipeline, code scan).