We are looking for an experienced AI Engineer to design and deliver scalable Gen AI solutions. The role involves building production-ready AI products using Python, React, and AWS, while leveraging contemporary architectures such as LLM pipelines, RAG, and agent-based systems.

This role is for subcontractors.

- Responsibilities:
- AI Engineer 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.
- Mandatory Skills Description:
- 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, Tableau
- • Knowledge of financial markets and enterprise data systems

📌 Senior AI engineer (Toronto)
🏢 Luxoft
📍 Toronto