Astra North Infoteck Inc.
Senior AI Engineer
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.
- Compensation
- Not disclosed
- Location
- Toronto, Canada
- Work setup
- Hybrid
- Seniority
- Senior
- Employment
- Not specified
- Experience
- 1+ years
- Category
- Ai engineering
- Posted
Skills
Must have
- 10+ years of progressive engineering experience (no less than 10 years will be considered)
- 1-2 years of direct experience leading emerging technology or enterprise AI initiatives
- Strong familiarity with SDLC best practices and DevSecOps protocols
- Core proficiency with commercial and open-source LLMs (GPT, Claude, Gemini, LLaMA), advanced prompt engineering, and LLM fine-tuning
- Deep hands-on experience with LangChain, LlamaIndex, Amazon Bedrock, Agentic AI, MCP, and Graph/RAG architectures
- Experience with Vector and Graph databases (Weaviate, Milvus, PGVector, Neo4J, Neptune), query optimization, and ETL orchestration with Apache Airflow/DAGs
- Comprehensive AWS knowledge: compute (EKS, Fargate, Lambda, EC2), networking/load balancing (ELB, GLB, NLB), data analytics (Athena, Glue, Lake Formation), and IaC (Terraform, Puppet, Docker)
- Strong Python proficiency (NumPy, Pandas, Matplotlib, Boto3) plus web development with Next.js, React, and TypeScript/JavaScript
- Familiarity with automated AI/web testing frameworks (Ragas, Playwright, Zephyr, Selenium) and BI tools (QuickSight, Tableau)
- Ability to work hybrid in Toronto Downtown (4 days work from office) with in-person interview
Full 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).