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.