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
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).
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 emerging technology or enterprise AI initiatives and develop AI solutions using commercial and open-source models, agentic systems, and Graph/RAG architectures. Build and support cloud-based AI applications, data pipelines, and full-stack web applications, applying SDLC, DevSecOps, testing, and analytics practices.
Requirements
The posting specifies 1–2 years of direct experience leading emerging technology or enterprise AI initiatives, along with strong SDLC and DevSecOps knowledge. It requires hands-on expertise across LLMs and AI frameworks, databases and orchestration, AWS infrastructure and DevOps, Python and modern web development, and testing and BI tools.
Skills
- Generative AI
- Large Language Models
- Prompt Engineering
- LLM Fine-Tuning
- LangChain
- LlamaIndex
- Amazon Bedrock
- Agentic AI
- Retrieval-Augmented Generation
- Vector and Graph Databases
- Apache Airflow
- AWS
- Terraform
- Python
- Next.js
- React
Visa sponsorship
Not detected in the job text
Categories
- Technology
- Software
- Data & Analytics
- Engineering
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 Architectures
- Weaviate
- Milvus
- PGVector
- Neo4j
- Amazon Neptune
- Apache Airflow
- AWS
- EKS
- Fargate
- Lambda
- Terraform
- Python
- Next.js
- React
- TypeScript
- DevSecOps