Sr AI Engineer

Apptoza Inc.; Toronto; On-site; CAD 140,000 - 200,000; Full time

Job summary

Apptoza Inc. in Toronto is seeking a Senior AI Architect to lead and contribute to the development of AI products, pilots, and enterprise solutions, focusing on clean, maintainable code and scalable architectures.

You will design Gen AI solutions, collaborate with product managers, data scientists, and domain experts, act as SME on Gen AI technologies, and guide end-to-end delivery using Agile practices while ensuring robust DevSecOps governance.

Qualifications

• 10+ years of progressive engineering experience.
• 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).
• Testing & Analytics: Familiarity with automated AI and web testing frameworks (Ragas, Playwright, Zephyr, Selenium) and Business Intelligence tools (QuickSight, Tableau).

Responsibilities

• Lead and actively contribute to the development of AI products, pilots, and enterprise solutions with a focus on writing clean, maintainable code.
• Design and architect scalable Generative AI solutions, including LLM pipelines and complex prompt-based applications.
• Collaborate closely with Product Managers, Data Scientists, and domain experts to translate business requirements into robust technical solutions.
• Act as an organization-wide SME on Gen AI technologies to shape long-term AI strategy and technical roadmap.
• Manage timelines, deliverables, and milestones using Agile practices (Scrum/Kanban) for end-to-end delivery.
• Monitor operational metrics and ensure adherence to DevSecOps governance and robust system reliability.

Skills

SDLC & DevSecOps

Generative AI & LLMs

LangChain & LlamaIndex

AWS Cloud Architecture

Graph/Vector DBs

MCP & Agentic AI

Apache Airflow

Scrum/Kanban

Tools

Weaviate

Milvus

PGVector

Neo4J

Neptune

Terraform

Puppet

Docker

Airflow

Job description

• 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).


• Testing & Analytics: Familiarity with automated AI and web testing frameworks (Ragas, Playwright, Zephyr, Selenium) and Business Intelligence tools (QuickSight, Tableau).