Senior GenAI Engineer – AWS, LLMs, Agentic AI, RAG Architectures

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.