About the role
Job Title : Senior Generative AI Engineer Location: Toronto, ON - (Hybrid) Duration: 6-12 months of contract with Possible extension
Position Overview: We are seeking a Senior AI Engineer to lead the design, development, and deployment of enterprise-scale Generative AI solutions. The ideal candidate will bring deep expertise in modern AI architectures, cloud-native engineering, and full-stack application development, with a track record of delivering production-grade AI platforms and emerging technology initiatives. This role requires hands-on leadership in building scalable GenAI applications, leveraging Large Language Models (LLMs), Agentic AI frameworks, RAG architectures, and cloud services within AWS. The successful candidate will collaborate closely with product, engineering, data, and business stakeholders to drive innovation and deliver measurable business outcomes.
Key responsibilities: Lead and actively contribute to the development of AI products, pilots and solutions, with a focus on clean, maintainable code using Python, React, and AWS tools. Design, architect and build scalable Gen AI solutions, including LLM pipelines, Agentic, MCP, Graph/RAG architectures, and prompt-based applications and emerging tech. Implement cloud-native solutions using AWS services such as EKS, Lambda, Fargate, Glue, and Athena. Optimize performance of AI products, Drive continuous learning and experimentation with cutting-edge Gen AI methods, frameworks, APIs, and toolchains. Work closely with product managers, data scientists, and domain experts to define technical solutions aligned with business needs. Act as a subject matter expert (SME) on Gen AI technologies and help shape the organization's AI roadmap. Own end-to-end delivery of Gen AI solutions. Manage timelines, deliverables, and project milestones using Agile practices (Scrum/Kanban). Monitor operational metrics and incident data to drive continuous improvement and reliability. Ensure adherence to governance, DevSecOps protocols.
Experience/Skiils: 10+ years of overall software engineering experience, with at least 6+ years in advanced engineering roles and 2+ years leading AI or emerging technology initiatives. Strong hands-on programming expertise in: Python React TypeScript/JavaScript Extensive experience with Generative AI technologies and LLM ecosystems, including: OpenAI GPT Anthropic Claude Google Gemini Llama models Deep understanding of: Prompt Engineering Agentic AI MCP (Model Context Protocol) Graph RAG Retrieval-Augmented Generation (RAG) AI orchestration patterns Expertise with AI frameworks and platforms such as: LangChain LlamaIndex Amazon Bedrock Strong AWS cloud engineering experience, including: EKS Lambda Fargate EC2 ELB/NLB Glue Athena Lake Formation Experience with Infrastructure as Code and container technologies: Terraform Docker Puppet Kubernetes Expertise in data engineering and orchestration tools: Apache Airflow DAG-based workflows Hands-on experience with Vector and Graph Databases: Weaviate Milvus PGVector Neo4j Amazon Neptune Strong understanding of database design, query optimization, and large-scale data processing. Experience with testing and evaluation frameworks: Ragas Playwright Selenium Zephyr
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About the role
Job Title : Senior Generative AI Engineer Location: Toronto, ON - (Hybrid) Duration: 6-12 months of contract with Possible extension
Position Overview: We are seeking a Senior AI Engineer to lead the design, development, and deployment of enterprise-scale Generative AI solutions. The ideal candidate will bring deep expertise in modern AI architectures, cloud-native engineering, and full-stack application development, with a track record of delivering production-grade AI platforms and emerging technology initiatives. This role requires hands-on leadership in building scalable GenAI applications, leveraging Large Language Models (LLMs), Agentic AI frameworks, RAG architectures, and cloud services within AWS. The successful candidate will collaborate closely with product, engineering, data, and business stakeholders to drive innovation and deliver measurable business outcomes.
Key responsibilities: Lead and actively contribute to the development of AI products, pilots and solutions, with a focus on clean, maintainable code using Python, React, and AWS tools. Design, architect and build scalable Gen AI solutions, including LLM pipelines, Agentic, MCP, Graph/RAG architectures, and prompt-based applications and emerging tech. Implement cloud-native solutions using AWS services such as EKS, Lambda, Fargate, Glue, and Athena. Optimize performance of AI products, Drive continuous learning and experimentation with cutting-edge Gen AI methods, frameworks, APIs, and toolchains. Work closely with product managers, data scientists, and domain experts to define technical solutions aligned with business needs. Act as a subject matter expert (SME) on Gen AI technologies and help shape the organization's AI roadmap. Own end-to-end delivery of Gen AI solutions. Manage timelines, deliverables, and project milestones using Agile practices (Scrum/Kanban). Monitor operational metrics and incident data to drive continuous improvement and reliability. Ensure adherence to governance, DevSecOps protocols.
Experience/Skiils: 10+ years of overall software engineering experience, with at least 6+ years in advanced engineering roles and 2+ years leading AI or emerging technology initiatives. Strong hands-on programming expertise in: Python React TypeScript/JavaScript Extensive experience with Generative AI technologies and LLM ecosystems, including: OpenAI GPT Anthropic Claude Google Gemini Llama models Deep understanding of: Prompt Engineering Agentic AI MCP (Model Context Protocol) Graph RAG Retrieval-Augmented Generation (RAG) AI orchestration patterns Expertise with AI frameworks and platforms such as: LangChain LlamaIndex Amazon Bedrock Strong AWS cloud engineering experience, including: EKS Lambda Fargate EC2 ELB/NLB Glue Athena Lake Formation Experience with Infrastructure as Code and container technologies: Terraform Docker Puppet Kubernetes Expertise in data engineering and orchestration tools: Apache Airflow DAG-based workflows Hands-on experience with Vector and Graph Databases: Weaviate Milvus PGVector Neo4j Amazon Neptune Strong understanding of database design, query optimization, and large-scale data processing. Experience with testing and evaluation frameworks: Ragas Playwright Selenium Zephyr