AI Engineer Specialist

Key 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.

Required Qualifications

• 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.

This position will be located in Toronto and offers the opportunity for a hybrid work environment at least 3 days a week in-office, subject to change, providing flexibility and accessibility for qualified candidates.

Come as You Are

Nasdaq is an equal opportunity employer. We welcome applications from candidates of all backgrounds and identities.

We are committed to fostering an inclusive workplace where diverse perspectives, experiences, and identities are valued and celebrated.

We ensure that individuals with disabilities are provided with reasonable accommodation throughout the hiring process.

What We Offer

We’re proud to offer a competitive rewards package that is meaningful, recognizes the unique needs of our employees and their families and incentivizes employees for their contribution to Nasdaq’s overall success.

The base pay range for this role is $96,000 - $132,000. In addition to base salary, Nasdaq provides a generous annual bonus/commission (short-term incentive), and equity (long-term incentive), comprehensive benefits, and opportunity for growth. Exact compensation may vary based on several job-related factors that are unique to each candidate, including but not limited to: skill set, experience, education/training, business needs and market demands.