Senior AI Engineer
- Toronto, ON, Canada
Full-time
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Job details
- Job type
- Full-time
- Apply by
- Experience
- 1–2 years of direct experience leading emerging technology or enterprise AI initiatives.
- Posting language
- English
- Posted
Original job description
Senior AI Engineer
Toronto Downtown, ON - Hybrid (4 Days WFO)
In Person Interview Required
Key Responsibilities
• AI Product Delivery: Lead and actively contribute to the development of AI products, pilots, and enterprise solutions with a focus on writing clean, maintainable code.
• Architecture & Design: Design and architect scalable Generative AI solutions, including LLM pipelines, Agentic workflows, Model Context Protocol (MCP), Graph/RAG architectures, and complex prompt-based applications.
• Cloud Infrastructure: Implement highly scalable, cloud-native solutions leveraging modern AWS services.
• Optimization & Innovation: Optimize the performance of production AI products. Drive continuous learning and experimentation with cutting-edge Gen AI methods, frameworks, APIs, and emerging toolchains.
• Cross-Functional Collaboration: Partner closely with Product Managers, Data Scientists, and domain experts to translate business requirements into robust technical solutions.
• Technical Leadership: Act as an organization-wide Subject Matter Expert (SME) on Gen AI technologies to help shape the company's long-term AI strategy and technical roadmap.
• Project Ownership: Manage timelines, deliverables, and project milestones using Agile practices (Scrum/Kanban) to ensure reliable end-to-end product delivery.
• Operations & Governance: Monitor operational metrics and incident data to improve system reliability. Ensure strict adherence to corporate governance and DevSecOps protocols.
Required Experience & Skills
Technical Expertise
• 10 + years of progressive engineering experience. - DO NOT SUBMIT LESS THEN 10 YRS EXP.
• 1–2 years of direct experience leading emerging technology or enterprise AI initiatives.
• Strong familiarity with the full Software Development Life Cycle (SDLC) best practices and DevSecOps protocols.
• Generative AI & LLMs: Core proficiency with commercial and open-source models (GPT, Claude, Gemini, LLaMA), advanced prompt engineering, and LLM fine-tuning techniques.
• AI Frameworks & Architectures: Deep hands-on experience with LangChain, LlamaIndex, Amazon Bedrock, Agentic AI, MCP, and Graph/RAG architectures.
• Databases & Orchestration: Proven experience with Vector and Graph databases (Weaviate, Milvus, PGVector, Neo4J, Neptune), query optimization, and ETL orchestration using Apache Airflow/DAGs.
• Cloud & DevOps (AWS): Comprehensive knowledge of AWS infrastructure, including computation (EKS, Fargate, Lambda, EC2), networking/load balancing (ELB, GLB, NLB), data analytics (Athena, Glue, Lake Formation), and Infrastructure as Code (Terraform, Puppet, Docker).
• Full-Stack Development: Strong proficiency in Python (NumPy, Pandas, Matplotlib, Boto3) alongside modern web application development using Next.js, React, and TypeScript/JavaScript.
• Testing & Analytics: Familiarity with automated AI and web testing frameworks (Ragas, Playwright, Zephyr, Selenium) and Business Intelligence tools (QuickSight, Tableau).
Toronto Downtown, ON - Hybrid (4 Days WFO)
In Person Interview Required
Key Responsibilities
• AI Product Delivery: Lead and actively contribute to the development of AI products, pilots, and enterprise solutions with a focus on writing clean, maintainable code.
• Architecture & Design: Design and architect scalable Generative AI solutions, including LLM pipelines, Agentic workflows, Model Context Protocol (MCP), Graph/RAG architectures, and complex prompt-based applications.
• Cloud Infrastructure: Implement highly scalable, cloud-native solutions leveraging modern AWS services.
• Optimization & Innovation: Optimize the performance of production AI products. Drive continuous learning and experimentation with cutting-edge Gen AI methods, frameworks, APIs, and emerging toolchains.
• Cross-Functional Collaboration: Partner closely with Product Managers, Data Scientists, and domain experts to translate business requirements into robust technical solutions.
• Technical Leadership: Act as an organization-wide Subject Matter Expert (SME) on Gen AI technologies to help shape the company's long-term AI strategy and technical roadmap.
• Project Ownership: Manage timelines, deliverables, and project milestones using Agile practices (Scrum/Kanban) to ensure reliable end-to-end product delivery.
• Operations & Governance: Monitor operational metrics and incident data to improve system reliability. Ensure strict adherence to corporate governance and DevSecOps protocols.
Required Experience & Skills
Technical Expertise
• 10 + years of progressive engineering experience. - DO NOT SUBMIT LESS THEN 10 YRS EXP.
• 1–2 years of direct experience leading emerging technology or enterprise AI initiatives.
• Strong familiarity with the full Software Development Life Cycle (SDLC) best practices and DevSecOps protocols.
• Generative AI & LLMs: Core proficiency with commercial and open-source models (GPT, Claude, Gemini, LLaMA), advanced prompt engineering, and LLM fine-tuning techniques.
• AI Frameworks & Architectures: Deep hands-on experience with LangChain, LlamaIndex, Amazon Bedrock, Agentic AI, MCP, and Graph/RAG architectures.
• Databases & Orchestration: Proven experience with Vector and Graph databases (Weaviate, Milvus, PGVector, Neo4J, Neptune), query optimization, and ETL orchestration using Apache Airflow/DAGs.
• Cloud & DevOps (AWS): Comprehensive knowledge of AWS infrastructure, including computation (EKS, Fargate, Lambda, EC2), networking/load balancing (ELB, GLB, NLB), data analytics (Athena, Glue, Lake Formation), and Infrastructure as Code (Terraform, Puppet, Docker).
• Full-Stack Development: Strong proficiency in Python (NumPy, Pandas, Matplotlib, Boto3) alongside modern web application development using Next.js, React, and TypeScript/JavaScript.
• Testing & Analytics: Familiarity with automated AI and web testing frameworks (Ragas, Playwright, Zephyr, Selenium) and Business Intelligence tools (QuickSight, Tableau).
More about this job
Responsibilities
Lead the design, development, and delivery of scalable Generative AI products and enterprise solutions, including LLM pipelines, agentic workflows, and Graph/RAG applications on AWS. Optimize production systems, collaborate across product and data teams, provide technical leadership, manage Agile delivery, and uphold operational reliability and governance.
Requirements
Requires 10 or more years of progressive engineering experience, including 1–2 years leading emerging technology or enterprise AI initiatives, plus strong SDLC and DevSecOps knowledge. Candidates should have hands-on expertise in Generative AI frameworks and architectures, AWS, databases and orchestration, Python and modern web development, and testing and analytics tools.
Skills
- Generative AI
- Large Language Models
- Prompt Engineering
- LLM Fine-Tuning
- LangChain
- LlamaIndex
- Amazon Bedrock
- Agentic AI
- Model Context Protocol
- Graph/RAG Architectures
- Vector and Graph Databases
- AWS
- Python
- React
- TypeScript
- DevSecOps
Visa sponsorship
Not detected in the job text
Categories
- Technology
- Software
- Data & Analytics
- Management & Leadership
Keywords
- Generative AI
- Large Language Models
- GPT
- Claude
- Gemini
- LLaMA
- Prompt Engineering
- LLM Fine-Tuning
- LangChain
- LlamaIndex
- Amazon Bedrock
- Agentic AI
- Model Context Protocol
- Graph/RAG
- Weaviate
- Milvus
- PGVector
- Neo4j
- Amazon Neptune
- Apache Airflow
- AWS
- EKS
- Fargate
- Lambda
- Terraform
- Docker
- Python
- Next.js
- React
- TypeScript