AI Engineer Specialist

Nasdaq•Toronto, ON

26d•$96,000 - $132,000•Hybrid

About The Position

As an AI Engineer Specialist reporting to the Sr. Director of Software Engineering, you'll play a critical role in designing, building, and deploying the AI systems and agents that power Nasdaq Lens – owning them end to end, from development through production. You'll thrive in this position if you're highly technical, curious, collaborative, and excited about language models and their capabilities. You'll join a fast-paced, product-oriented team that ships new AI capability essentially every month and actively maintains it working directly with clients, product and engineering teams to identify and build what's next. The primary focus of the role is continuous AI system and agent development, followed by deployment and operation in AWS, with additional responsibility for supporting the platform’s database and data-processing layer.

Requirements

Bachelor's degree in Computer Science, Engineering, or equivalent, with 4–7 years of professional software engineering experience building and shipping production systems.
Expert-level Python development experience, with strong knowledge of automation and orchestration frameworks.
Hands-on experience building AI agent harness frameworks, retrieval systems, SQL and relational databases
Comfortable working with CI/CD pipelines, Git, and integrating systems through REST APIs and MCPs
Comfortable deploying and operating applications on AWS, or eager to ramp up quickly on our stack.
Working knowledge of SQL and relational databases (queries, views, basic schema design).
Strong analytical and systems-thinking skills
Excellent written and verbal communication, with the ability to work independently in ambiguous environments and collaborate across global teams
Familiarity with financial services, fintech, or other regulated industries, or the judgment to work carefully within regulated constraints.
Hands-on experience with leading AI coding assistants (e.g., GitHub Copilot, Claude Code) in a day-to-day development workflow.

Responsibilities

Design and build LLM-powered agents and applications – from focused assistants to complex, multi-step agentic systems – and own the agent harness: the core loop and the capabilities layered on top of it that make agents reliable for real work.
Architect how agents manage context and knowledge over long-running tasks, including memory, domain skills, retrieval, summarization, and context isolation, so they stay safe, accurate and coherent well beyond a single context window.
Establish evaluation, tracing, and observability so agent behavior can be measured, debugged, and continuously improved from real usage.
Choose and evolve the right frameworks, runtimes, and harnesses for the job, and set the patterns the rest of the team builds on.
Partner with product and clients to turn needs into shipped capability, iterating quickly on feedback.
Document architectures and workflows, research emerging AI technologies, and build proofs of concept to evaluate new capabilities.
Deploy and run agents and services in production on AWS using our existing stack and patterns – including agent-runtime platforms such as Amazon Bedrock AgentCore alongside supporting services
Use Terraform and infrastructure-as-code to provision and manage resources the enterprise way – version-controlled, reviewed, and repeatable
Write API integrations that connect agents to internal platforms and data.
Work within our CI/CD pipelines to ship changes safely, and help extend them as needs grow.
Maintain and optimize the databases and data-processing that feed the agents – writing and tuning queries, maintaining views, and keeping retrieval sources clean and current.
Build and maintain the data pipelines that keep agent knowledge up to date.
Help evolve schemas and data models as the product grows.