CONFLUX SYSTEMS Verified
Information Technology, Software Development, Data Engineering, Consulting
GenAI Engineer with Data Engineering expertise
Toronto, Ontario, CanadaHybridContractPosted 2 months ago
Role summary
We are seeking a GenAI Engineer with strong Data Engineering expertise to join our team in Toronto. This hybrid role focuses on leveraging generative AI to solve complex business problems. You will be responsible for designing and implementing data processing pipelines, building microservice architectures for GenAI solutions, and deploying them on the Azure cloud platform. Proficiency in Python, SQL, and Azure services like Databricks and Data Factory is essential. Experience with RAG patterns and optimizing model performance is highly preferred. This role requires effective communication and collaboration with cross-functional teams to deliver results in a dynamic environment.
- Role / Skills – GenAI Engineer with Data Engineering expertise
- Location – Canada, Toronto – Hybrid
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.
- Communication and Collaboration: Strong communication and ability to effectively collaborate with multi-functional teams and partners.
- Motivation and Results-Driven: Highly motivated and results-driven, comfortable in handling changes, and delivering results in complex situations.
