AWS Agentic AI Developer Job Toronto area,Ontario Canada,Software Development

Extensive hands-on development experience in Agentic AI projects on AWS native services is required

This role is flexible within proximity to the company office in Toronto to accommodate a hybrid work model.

The Emerging Technologies team in Global Technology and Operations (GTO) Canada at the company is a trusted Data, Cloud and AI advisor and go-to implementation partner for our global clients' Data and Advanced Analytics needs. We're an entrepreneurial team on a continuous mission to position the company as the best‑in‑class AI partner and develop new and exciting opportunities in latest technologies.

Our capabilities in Agentic AI are constantly evolving with current focus on AI agents governance and mitigating risks associated with Agentic AI.

Are you an Agentic AI candidate that has not just played around with Large Language Models, Copilots and Coding Assistants but also has experience building hands‑on multi‑agent orchestration based Agentic AI solutions?

Working with us, you will be supported by our best and brightest AI Scientists and Architects working on challenging projects in Agentic AI.

Your future duties and responsibilities

Serve as an Intermediate AI/ML Engineer for agent‑based initiatives, including scoping, estimation, and initial architectural design of AI & Business Applications.
Design, develop, and deploy intelligent agents using AWS Bedrock, Lang Chain, Kiro, Graph DB, Strands, and related agent frameworks.
Build and maintain applications using AWS agent core framework, orchestration layers, and hierarchical agent workflows to support complex reasoning and automation.
Develop secure, scalable APIs using AWS Lambda and API Gateway to expose AI and agent capabilities to downstream systems.
Implement prompt engineering best practices to optimize model performance, accuracy, and reliability across use cases.
Design and implement RAG (Retrieval Augmented Generation) pipelines leveraging vector databases and knowledge graphs.
Monitor, evaluate, and optimize LLM performance, cost, latency, and reliability in production environments.
Support testing cycles by validating AI outputs, identifying gaps, and improving agent behavior through tuning and iteration.
Ensure solutions meet enterprise standards for security, compliance, and responsible AI usage.

This role comes with sometimes intense delivery expectations, churning out POVs for clients in a short period of time and then going on to implement a full‑scale production solution ensuring right user experience and adoption.

Required qualifications to be successful in this role

Hands‑on work experience with implementing Agentic AI business process automation workflows in at least 1‑2 different projects.
Strong experience with Python & Spark for building AI, data, and agent‑based applications.
Hands‑on experience with AWS Bedrock and large language models (LLMs).
Experience using Lang Chain, Strands, or similar agent frameworks.
Strong understanding of agent core design, multi‑agent systems, and orchestration patterns.
Experience building serverless solutions using AWS Lambda and API Gateway.
Solid knowledge of prompt & context engineering techniques and LLM optimization strategies.
Experience integrating LLMs with enterprise systems via APIs.
Build solutions to any complex business problem using AI‑Development Life Cycle methodology.
Strong problem‑solving, communication, and collaboration skills.

Preferred qualifications

Experience with broader AI and agent frameworks, including hierarchical agents and autonomous agent architectures.
Familiarity with Model Context Protocol (MCP) and emerging agent interoperability standards.
Experience with Kiro or similar AI IDE tools for agent‑assisted development workflows.
Experience with CI/CD pipelines, Infrastructure as Code (CDK, Cloud Formation, or Terraform), and version control (Git).
Familiarity with containerization technologies such as Docker, Amazon ECS, or EKS for production AI deployments.
Experience with AWS Quick Tools and AWS Frontier Agents.
Hands‑on experience with deep research agents and multi‑step reasoning workflows.
Experience implementing RAG using Vector Databases (e.g., Open Search, Pinecone,…