GenAI Engineer with Data Engineering expertise
Role summary
This is a long-term contract role for a GenAI Engineer with Data Engineering expertise, based in Toronto, Canada, operating in a hybrid work model. The role focuses on leveraging generative AI models to solve complex business problems, with a strong emphasis on data indexing, chunking, and RAG patterns. Key responsibilities include managing releases for AI applications using CI/CD pipelines, building microservice architectures for GenAI solutions, and implementing efficient data processing pipelines for large-scale data sources. Expertise in the Azure cloud environment, including Databricks, Data Factory, and Storage accounts, along with proficiency in Python and SQL, is required. Familiarity with LLM deployment and model performance monitoring is also desired.
- Job Title – GenAI Engineer with Data Engineering expertise
- Location – Canada, Toronto – Hybrid
- Long Term Contract
Preferred Skillset:
- Generative AI Knowledge: Solid understanding of generative AI models and algorithms, and their applications in solving complex business problems. Preferred candidate must have experience in data indexing, chunking and optimizing RAG patterns.
- Release Management: Experience with scaled-up release management for AI applications using CICD pipelines for automated testing and deployment
- Technical skills: Experience with Azure cloud environment with Databricks, Data Factory, Storage account, Proficiency in Python programming & SQL
- Microservice Architecture: Experience in building microservice-based architecture for GenAI solutions.
- Data Processing: Experience in working with large-scale data sources in complex environments and implementing scalable and efficient data processing pipelines.
- Azure Expertise: Expert knowledge in full stack Azure development, including experience with Azure cloud platforms in the context of GenAI solution development and deployment, and familiarity with common tools in data science.
- Platform Experience: Nice to have knowledge of optimizing model performance and ensuring scalability to handle expected workloads. Familiarity with LLM models deployment in the cloud environment. Ability to set up monitoring for model performance, including metrics like accuracy, latency, and throughput.
Bal Krishna Pathak
Team Lead-Quantum World Technologies Inc.
Contact: 647-875-4892
