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GenAi Developer

CYNET SYSTEMS•Toronto, Ontario
Full-time
CAD 94K
per year
👁️ 0 views•📝 0 applications•Posted 10/10/2026•Expires 11/9/2026
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Job Description

Job Overview: Must to Have: • Design and develop efficient maintainable and reusable Python scripts for data extraction transformation and loading ETL in GenAI application. • Demonstrate strong proficiency in the Python programming language. • Collaborate with cloud platforms eg AWS Azure GCP to build Generative AI GenAI applications. • Develop implement and maintain APIs to integrate GenAI models into applications and workflows. • Conduct research and experiments to explore innovative generative. • AI techniques such as AI agents hybrid RAG optimization and workflow orchestration. • Apply expertise in generative AI concepts including document storage chunking vector databases RAG implementation and basic finetuning methods. Nice to Have: • Champion DevOps and MLOps practices focusing on continuous integration deployment and AI model monitoring. • Previous experience leveraging tools like Docker Kubernetes and Git to build and manage AI pipelines. • Experience implementing monitoring and logging solutions to ensure the performance and reliability of AI models. • Collaboration experience with software engineering and operations teams for seamless AI model integration and deployment. • Possess familiarity with DevOps and MLOps methodologies emphasizing CICD processes and AI model lifecycle management. Founded in 2010 and headquartered in the Washington, DC metro area, Cynet Systems Inc. is a leading staffing and recruiting powerhouse. Proudly recognized as a nationally and locally certified diversity firm, Cynet delivers agile, scalable talent solutions across industries. With an active footprint in all 50 U.S. states and Canada, we support thousands of consultants through our expansive, high-performing recruitment engine operating across North America and Asia—ensuring speed, quality, and consistency in every hire.

Required Skills

PythonAWSAzureGCPAPIsGenerative AIdocument storagechunkingvector databasesRAG implementationfinetuningDevOpsMLOpsDockerKubernetesGitmonitoringloggingCICDAI model lifecycle management

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