Senior GenAI Engineer – AWS | LLMs | Agentic AI | RAG Architectures
About the role
Senior GenAI Engineer – AWS | LLMs | Agentic AI | RAG Architectures
Toronto, ON – Hybrid (2–3 Days WFO)
• 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 and DevSecOps protocols.
Experience / Skills
• 6+ years of progressive experience in engineering roles, including at least 1–2 years leading emerging tech or AI initiatives. • Gen AI models (GPT, Claude, Gemini, LLaMA) and prompt engineering techniques. • Agentic AI, MCP, and Graph/RAG architectures. • Gen AI Framework (LangChain, LlamaIndex, Amazon Bedrock). • Web application development using Next.js, React, TypeScript/JavaScript. • AWS cloud services (EC2, ELB/GLB/NLB, EKS, Fargate, Lambda, Athena, Glue, Lake Formation). • Infrastructure as Code (Puppet, Terraform, Docker) and containerized deployments. • ETL orchestration using Apache Airflow/DAGs. • Vector/Graph databases (Weaviate, Milvus, PGVector, Neo4J, Neptune) and query optimization. • Python programming (NumPy, Pandas, Matplotlib, Boto3). • Automated testing frameworks (Ragas, Playwright, Zephyr, Selenium). • Familiarity with SDLC best practices, DevSecOps, Agile Scrum/Kanban, and work management tools (JIRA, Confluence, JIRA Align). • Knowledge of LLM fine-tuning techniques. • Experience in BI tools like QuickSight and Tableau. • Knowledge of financial markets and enterprise data systems.
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Senior GenAI Engineer – AWS | LLMs | Agentic AI | RAG Architectures
About the role
Senior GenAI Engineer – AWS | LLMs | Agentic AI | RAG Architectures
Toronto, ON – Hybrid (2–3 Days WFO)
• 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 and DevSecOps protocols.
Experience / Skills
• 6+ years of progressive experience in engineering roles, including at least 1–2 years leading emerging tech or AI initiatives. • Gen AI models (GPT, Claude, Gemini, LLaMA) and prompt engineering techniques. • Agentic AI, MCP, and Graph/RAG architectures. • Gen AI Framework (LangChain, LlamaIndex, Amazon Bedrock). • Web application development using Next.js, React, TypeScript/JavaScript. • AWS cloud services (EC2, ELB/GLB/NLB, EKS, Fargate, Lambda, Athena, Glue, Lake Formation). • Infrastructure as Code (Puppet, Terraform, Docker) and containerized deployments. • ETL orchestration using Apache Airflow/DAGs. • Vector/Graph databases (Weaviate, Milvus, PGVector, Neo4J, Neptune) and query optimization. • Python programming (NumPy, Pandas, Matplotlib, Boto3). • Automated testing frameworks (Ragas, Playwright, Zephyr, Selenium). • Familiarity with SDLC best practices, DevSecOps, Agile Scrum/Kanban, and work management tools (JIRA, Confluence, JIRA Align). • Knowledge of LLM fine-tuning techniques. • Experience in BI tools like QuickSight and Tableau. • Knowledge of financial markets and enterprise data systems.