GenAI Lead Developer

CYNET SYSTEMS; Toronto; On-site; CAD 80,000 - 100,000; Full time

Job summary

A technology solutions provider in Toronto seeks a skilled Python Developer to design and develop efficient scripts for data extraction and loading in generative AI applications. The role involves collaborating with cloud platforms like AWS, Azure, and GCP, and applying expertise in API development and generative AI concepts. Ideal candidates will demonstrate strong Python proficiency and have experience with DevOps practices. This position offers opportunities to explore innovative AI techniques and contribute to cutting-edge projects.

Qualifications

• Strong proficiency in Python for data extraction, transformation, and loading.
• Experience with cloud platforms like AWS, Azure, and GCP.
• Understanding of generative AI concepts and models.

Responsibilities

• Design and develop Python scripts for ETL in applications.
• Collaborate with cloud platforms to build GenAI applications.
• Conduct research on innovative generative AI techniques.

Skills

Python programming

Data extraction and transformation

Collaborating with cloud platforms

API development

Generative AI concepts

Tools

Docker

Kubernetes

Git

AWS

Azure

GCP

Job description

Job Description

• 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 (e.g., 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 fine-tuning 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 CI/CD processes and AI model lifecycle management.