Senior AI Engineer
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Summary
Senior engineer in Toronto (hybrid, 4 days in office, in-person interview) building generative AI products and enterprise solutions: LLM pipelines, agentic workflows, MCP and Graph/RAG architectures on AWS. Full-stack work spans Python, LangChain/LlamaIndex, Bedrock, vector/graph databases, and Next.js/React/TypeScript. Requires 10+ years' experience.
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).
Skills
- Agentic AI
- Agile
- AI
- Airflow
- Analytics
- API
- Athena
- AWS
- AWS Bedrock
- Cloud
- Cloud Native
- Data Analytics
- DevOps
- DevSecOps
- Docker
- EC2
- EKS
- ETL
- Fine Tuning
- Generative AI
- Infrastructure as Code
- JavaScript
- Kanban
- Lambda
- LangChain
- LlamaIndex
- LLM
- Matplotlib
- MCP
- Milvus
- Neo4j
- Networking
- Next.js
- NumPy
- pandas
- pgvector
- Playwright
- Prompt Engineering
- Puppet
- Python
- RAG
- React
- Scrum
- SDLC
- Selenium
- Tableau
- Terraform
- TypeScript
- Weaviate